This presentation compares 2-parameter and 3-parameter Weibull analysis on cable flex test data. Using both real cable test data and generated Weibull data, the presenters found that the 3-parameter model generally provided a better fit. With larger sample sizes, the confidence intervals on the threshold parameter decreased. While 2-parameter analysis often resulted in a steeper estimated slope, 3-parameter analysis was found to better characterize the cable data, especially with smaller sample sizes. The presenters concluded that 3-parameter Weibull analysis is preferable for analyzing cable flex test results.
This document provides an overview of reliability functions for life testing in Minitab. It discusses selecting a probability distribution, testing units to failure with right or arbitrary censoring, accelerated testing with single or multiple factors, and identifying the best-fitting distribution. The agenda includes introductions to reliability, probability distribution functions, parameters, censoring, distribution identification, and a case study of wire scrape testing to identify the best-fitting distribution for cable life data.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W4 Reliability J. García - Verdugo
The document discusses reliability, including definitions of reliability, reliability phases, reliability importance, reliability calculations for serial and parallel systems, and Weibull analysis. Reliability is defined as the probability that a product or system will function as intended without failure over a specified period of time. There are generally three failure phases: infant mortality with early high failure rates, random failures, and wear out with increasing failure rates over time. Reliability is important for customers, cost savings, and competitiveness. Calculations can determine the reliability of serial and parallel systems based on component reliabilities. Weibull analysis involves plotting failure data to determine the appropriate failure distribution.
Forecasting warranty returns with Wiebull FitTonda MacLeod
Analyze Wise provides a statistical analysis of warranty return data to forecast future returns using a Weibull distribution model. The analysis involves obtaining time-to-failure data from historical warranty returns, performing a regression to identify the best fitting distribution model and associated parameters, and using the model to predict return counts by time period. The forecasts can help companies plan repair resources, manage customer relationships, and evaluate warranty expenses and product performance.
Practical Use of Stress-Strength Models to develop SpecificationsRob Schubert
The world is full of random events, and these cause stresses on products. If you know the strength of a product and the return rate, you can develop a stress profile. The more similar products you have, the better this can be. From this profile, a target can be developed for future products to meet, and calculate the expected return rates. Also you can use this profile to estimate the impact of product improvements on warranty.
STA457 Assignment Liangkai Hu 999475884Liang Kai Hu
The document analyzes the time series properties of property index returns (NPI) and other asset class returns. It finds NPI exhibits inertia due to infrequent transactions. To address this, it uses autoregressive (AR) and moving average (MA) models to unsmooth the NPI data. The best-fitting models are AR(7) and MA(8). It then estimates factor loadings by regressing unsmoothed NPI returns on Fama-French factors. While some factors are statistically significant, model fits remain poor based on other diagnostics. The document explores using principal component analysis to improve factor fitting.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Measurement System ...J. García - Verdugo
This document discusses measurement system analysis for continuous measurements. It introduces the Gage R&R study as a tool to assess measurement systems. Key indices for evaluating measurement systems are the Percentage of Tolerance (P/T) and Percentage of Range and Repeatability (%R&R). P/T assesses how much of the specification tolerance is used by measurement error while %R&R evaluates measurement error relative to total process variation. The document provides guidelines for properly conducting a Gage R&R study and interpreting its results.
This document provides an overview of reliability functions for life testing in Minitab. It discusses selecting a probability distribution, testing units to failure with right or arbitrary censoring, accelerated testing with single or multiple factors, and identifying the best-fitting distribution. The agenda includes introductions to reliability, probability distribution functions, parameters, censoring, distribution identification, and a case study of wire scrape testing to identify the best-fitting distribution for cable life data.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W4 Reliability J. García - Verdugo
The document discusses reliability, including definitions of reliability, reliability phases, reliability importance, reliability calculations for serial and parallel systems, and Weibull analysis. Reliability is defined as the probability that a product or system will function as intended without failure over a specified period of time. There are generally three failure phases: infant mortality with early high failure rates, random failures, and wear out with increasing failure rates over time. Reliability is important for customers, cost savings, and competitiveness. Calculations can determine the reliability of serial and parallel systems based on component reliabilities. Weibull analysis involves plotting failure data to determine the appropriate failure distribution.
Forecasting warranty returns with Wiebull FitTonda MacLeod
Analyze Wise provides a statistical analysis of warranty return data to forecast future returns using a Weibull distribution model. The analysis involves obtaining time-to-failure data from historical warranty returns, performing a regression to identify the best fitting distribution model and associated parameters, and using the model to predict return counts by time period. The forecasts can help companies plan repair resources, manage customer relationships, and evaluate warranty expenses and product performance.
Practical Use of Stress-Strength Models to develop SpecificationsRob Schubert
The world is full of random events, and these cause stresses on products. If you know the strength of a product and the return rate, you can develop a stress profile. The more similar products you have, the better this can be. From this profile, a target can be developed for future products to meet, and calculate the expected return rates. Also you can use this profile to estimate the impact of product improvements on warranty.
STA457 Assignment Liangkai Hu 999475884Liang Kai Hu
The document analyzes the time series properties of property index returns (NPI) and other asset class returns. It finds NPI exhibits inertia due to infrequent transactions. To address this, it uses autoregressive (AR) and moving average (MA) models to unsmooth the NPI data. The best-fitting models are AR(7) and MA(8). It then estimates factor loadings by regressing unsmoothed NPI returns on Fama-French factors. While some factors are statistically significant, model fits remain poor based on other diagnostics. The document explores using principal component analysis to improve factor fitting.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Measurement System ...J. García - Verdugo
This document discusses measurement system analysis for continuous measurements. It introduces the Gage R&R study as a tool to assess measurement systems. Key indices for evaluating measurement systems are the Percentage of Tolerance (P/T) and Percentage of Range and Repeatability (%R&R). P/T assesses how much of the specification tolerance is used by measurement error while %R&R evaluates measurement error relative to total process variation. The document provides guidelines for properly conducting a Gage R&R study and interpreting its results.
Trends in the Backend for Semiconductor Wafer InspectionRajiv Roy
The document discusses trends in back-end semiconductor inspection for the automotive industry in 2008. It covers increasing use of inspection for zero defects programs, tool matching and correlation, tall particle detection to prevent probe card damage, inspection of CMOS sensors and TSVs, and microbump and copper pillar bump inspection. Key points emphasized are the need for inspection tools designed for tool matching, implementing golden standards, and combining 2D and 3D inspection.
LVTS Advanced matching matching concept for CDSEMVladislav Kaplan
Motivation:
Significant challenges for various CD measurement matching procedures are reaching a comparable complexity as result of negative effects of roughness on the features. Due to the constant trend of integrated circuit in features reduction, impact of roughness start to be more destructive for various sets of measurement algorithms. Commonly used attempts to increase magnification for pattern recognition in addressing mode could in turn detect higher deviation from predefined patterns and thus initiate shift in placement of measurement gate.
Description of the approach:
The purpose of this paper is to discuss how to reduce measurement gate placement variation
impact and filter acquired data using edge correlation approach – creation of width correlation
function represents particular feature under test and it’s comparison to “golden” one as a mean of
detection of uncorrelated scans, which in turn should be excluded from overall computation of
matching results.
We describe general approach for algorithm stepping and various techniques for judgment of measurement comparison validity. Presented approach also has particular interest in determination of specified tool performance for predefined pattern recognition feature as well as for pattern recognition algorithm robustness study - direct interest for manufacturer.
Evaluation of results:
Precise matching estimation as part of Round Robin routines creating possibility to work with
restricted amount of data and perform quick reliable qualification procedures.
This paper concentrated on practical approach and used both simulation data and actual
measurement data before and after proposed optimization taken by various generation tools by
Hitachi (S-8840, S-9300, S-9380) in production environment.
Criterion Unacceptable Minimum Satisfactory Excellent Weight
Topic and Introduction The topic has little relevancy
in the specified area and no
problem statement and the
abstract did not give any
information about what to
expect in the report.
The topic has somewhat
relevancy and/or the
problem statement was
poorly constructed,
and/or the abstract
provides little information
on the project
Relevant topic is
selected and the
problem statement is
appropriately
constructed and/or the
abstract provides
adequate information on
the project
Relevant topic is selected and
the problem statement is well
constructed and the abstract
is concise and provides
adequate information on the
project
20
Score 0 7 17 20
Writing Quality The writing is incoherent,
broken, overly long, and
contains many spelling or
grammatical errors
The writing is incoherent,
lengthy, and has some
spelling or grammar
errors
The writing is coherent,
and only has a few
spelling or grammar
errors
The writing is coherent,
concise, free of spelling errors
and grammatically correct
10
score 0 5 8 10
Technical Accuracy Work is not accurate. Work has minimal
accuracy
Work is mostly accurate
with less than two minor
errors
Work is accurate and well
constructed 30
score 0 15 26 30
Clarity of Illustrations,
Diagrams or Charts
Figures, diagrams, tables
are sloppy, and/or not
accurate, and are not
labeled.
Figures, diagrams are
not especially clear, and
but labels and diagrams
are accurate.
Figures, diagrams,
tables are clearly drawn,
clearly labeled, accurate
Figures, diagrams, tables are
clearly drawn, clearly labeled,
accurate. Labels are
descriptive. Diagrams are
exceptionally detailed.
15
Score 0 8 12 15
Solution
& Conclusion Was not logically or
effectively structured and
presents an illogical
explanation for findings.
Needs greater effort to
make it a well-
constructed paper and
the findings were not
logically presented.
Were logically organized
and made good
connections among
ideas. Presents a
logical explanation for
findings.
Information is logically and
creatively organized with
smooth transitions. Presents
a logical explanation for
findings.
25
0 15 21 25
META RUBRIC FOR LAB REPORTS
G:\Online Course Management\SBT Meta Rubrics\Lab Report Meta Rubric.xlsx
ELEC 161 – Module 2 Laboratory - Page 1
ELEC 161 Electronics II
Module 2 Lab: Ideal vs. Real Operational Amplifiers
Introduction.- In this lab we will explore the real Operational Amplifier as opposed to the Ideal Op Amp. We
will study the different parameters that make Operational Amplifiers different across models and will measure
different types of Op Am ps.
Procedure
1.- Offset Voltage
Consider a voltage amplifier with a certain gain Av. We can write Vo = Av * Vin, so when Vin =0, then we expect
the out voltage (Vout) to b.
The document discusses factors that influence voiding in solder joints, including solder paste type, stencil design, and reflow profile. Testing found that solder paste B and a 5-dot stencil pattern produced higher voiding and larger voids compared to other variables. Reflow profile also impacted voiding depending on the solder paste. Vapor phase reflow with vacuum was shown to significantly reduce voiding and could be used to rework voids. Future work will further study ways to minimize voiding, such as different solder powders, nitrogen reflow, and additional stencil designs.
Instruments basic training for iti,dme .pptSameerSutar8
The document provides information on instrument calibration training objectives including awareness of calibration, acceptance criteria, calibration methods, standards used, proper measurement techniques, instrument selection and use. It describes calibration processes and criteria for various instruments like vernier calipers, micrometers, dial indicators, bore gauges, electronic level meters, surface plates, and others. Key aspects checked include linearity, parallelism, accuracy, sensitivity, and specifications are provided for acceptance. Maintaining proper calibration is important to ensure measurement accuracy and instrument reliability.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W4 Monte Carlo Simulat...J. García - Verdugo
This document describes using Monte Carlo simulation to analyze variations in manufacturing processes and electrical circuits. It discusses generating random input variables based on their distributions, calculating output results using equations, and analyzing the output distribution to determine process capability and variation. An example simulates variations in five stacked metal parts and calculates the total dimension distribution. Another simulates variations in resistor values in an electrical circuit and calculates the distribution of the output voltage.
AN APPARATUS BASED APPROACH FOR COMPREHENSIVE MEASUREMENT OF BRIGHT BAR PARAM...IRJET Journal
The document describes the development of an apparatus for comprehensively measuring various parameters of bright bars, such as overall diameter, circularity error, straightness error, amount of bending, and number of surface cracks. The apparatus uses techniques like optical sensors, dial indicators, and UV lamps to automatically measure the parameters, providing more efficient inspection than manual methods. Test results demonstrated the machine could accurately measure bright bar parameters in less time than traditional individual instrumentation.
This document describes the design of an SWR/Wattmeter device used by ham radio operators to measure transmission strength and quality. It discusses the hardware and software design considerations including using a directional coupler to measure forward and reflected power, a microprocessor to calculate SWR and control display elements, and voltage regulation circuitry. The document also provides testing results showing the design meets constraints for accuracy across frequency ranges and power levels.
A presenation on a technique that was developed to correlate Adjecent channnel rejection of a VHF reiciever to a new method that involves a much simpler technique that can be deployed on production ATE testers
15 - Introduction to Optimization Tools Rev A.pptMohamedShabana37
This document provides an overview of TEMS Investigation and TEMS Visualization, two optimization tools from Ericsson. TEMS Investigation allows users to collect, analyze, and post-process network data to verify and optimize UMTS, GSM, GPRS, and EDGE networks. It helps troubleshoot issues like dropped calls, coverage imbalance, pilot pollution and missing neighbors. TEMS Visualization analyzes statistics from Ericsson's OSS to identify problems like missing neighbors, pilot polluters and call issues using a call event analyzer and other features. The document describes the capabilities and interface of both tools.
Scalable NDT Instruments for the Inspection of Variable Geometry ComponentsOlympus IMS
For the past several years, the aviation industry has seen above normal growth due, in part, to lower oil prices, saving major aircraft operators millions of dollars. As a result of this outstanding growth, production rates for new airplanes have increased and new aircraft programs are being launched. Consequently, aviation component manufacturers are facing new challenges including a rise in production rates, a high probability of detection (POD) due to the critical nature of the parts being manufactured, lack of skilled operators, and parts with increasingly complex geometry.
Ultrasonic phased array (PA) instruments have evolved, enabling an increase in inspection speeds and the implementation of advanced acquisition strategies. The introduction of scalable instruments and advanced acquisition strategies helps manufacturers address the inspection challenges they are facing. Scalability can now be used for nondestructive testing (NDT), enabling system integrators and manufacturers to improve the performance of their solutions by using multiple instruments in parallel. The evolution of electronic components enables advanced acquisition strategies, such as adaptive ultrasound, to be implemented. Adaptive ultrasound simplifies the inspection of complex components and improves the POD by using innovative signal-processing algorithms.
This paper presents an overview of scalable NDT instruments with the goal of helping NDT integrators and manufacturers to address the challenges they are facing in terms of system performance, production output, and quality control.
The document discusses methods for determining sample sizes in reliability testing. It covers two main approaches: the estimation approach which aims to control the confidence interval width, and the risk control approach which aims to control type I and type II errors. Examples are provided to demonstrate how to use each approach to determine the needed sample size given parameters like required reliability, confidence level, allowable failures. Both parametric and non-parametric methods are introduced for different test scenarios. Software tools can help calculate the sample sizes required to meet the test objectives.
TECNALIA presented in the European Utility Week 2014 (Amsterdam), the conference “Electromagnetic Disturbances and Blocking Problems in PLC Technologies”. Ibon Arechalde analyzed the problems with noises and impedances in PLC technologies, how TECNALIA´s EMC and Telecom Laboratory can evaluate devices in aggressive environments and how to mitigate the negative effects of noise and impedance in the electrical networks.
http://www.tecnalia.com/technological-services/
This document summarizes a digital signal processing project that involves resampling audio signals and modeling signals using autoregressive (AR) processes.
The resampling part involves downsampling two audio signals with correct and incorrect sampling rate conversions. Graphs and analysis show the resampled signals have lower quality and more distortion compared to the originals.
The AR modeling part estimates AR model coefficients from one of the signals using the Yule-Walker equations. A filter is designed to "whiten" the signal, removing noise. Graphs and audio comparison show the filtered signal has less noise but also some quality loss.
Tailor welding blanks generally used for making doors of an automobile get crack along welding line. This leads to rejection and Wrong body production. Its analysis & countermeasure shared in sides.
The document discusses an investigation into defects found in spoolshaft parts produced on machines Huffman #11 and #16. Measurements of flat widths on sample parts from both machines showed concentrations of out-of-specification measurements in certain locations around the spoolshaft. Analysis determined the measurement system was capable of precise measurements. Root cause analysis found the process variation between parts was too large, even though the process mean was within tolerance limits. Corrective action proposed lowering the lower specification limit by 20μm to reduce scrap rates given the current machine capability.
This document discusses methods for performing gauge repeatability and reproducibility (GR&R) tests on surface metrology equipment. It finds that GR&R values are often over 100% due to within-part variation rather than instrument error. Using a uniform sinusoidal specimen eliminates much of the within-part variation, resulting in more accurate GR&R values typically under 10%. The document also provides a method to determine the optimal number of measurements needed per part based on the surface variation and tolerance.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Trends in the Backend for Semiconductor Wafer InspectionRajiv Roy
The document discusses trends in back-end semiconductor inspection for the automotive industry in 2008. It covers increasing use of inspection for zero defects programs, tool matching and correlation, tall particle detection to prevent probe card damage, inspection of CMOS sensors and TSVs, and microbump and copper pillar bump inspection. Key points emphasized are the need for inspection tools designed for tool matching, implementing golden standards, and combining 2D and 3D inspection.
LVTS Advanced matching matching concept for CDSEMVladislav Kaplan
Motivation:
Significant challenges for various CD measurement matching procedures are reaching a comparable complexity as result of negative effects of roughness on the features. Due to the constant trend of integrated circuit in features reduction, impact of roughness start to be more destructive for various sets of measurement algorithms. Commonly used attempts to increase magnification for pattern recognition in addressing mode could in turn detect higher deviation from predefined patterns and thus initiate shift in placement of measurement gate.
Description of the approach:
The purpose of this paper is to discuss how to reduce measurement gate placement variation
impact and filter acquired data using edge correlation approach – creation of width correlation
function represents particular feature under test and it’s comparison to “golden” one as a mean of
detection of uncorrelated scans, which in turn should be excluded from overall computation of
matching results.
We describe general approach for algorithm stepping and various techniques for judgment of measurement comparison validity. Presented approach also has particular interest in determination of specified tool performance for predefined pattern recognition feature as well as for pattern recognition algorithm robustness study - direct interest for manufacturer.
Evaluation of results:
Precise matching estimation as part of Round Robin routines creating possibility to work with
restricted amount of data and perform quick reliable qualification procedures.
This paper concentrated on practical approach and used both simulation data and actual
measurement data before and after proposed optimization taken by various generation tools by
Hitachi (S-8840, S-9300, S-9380) in production environment.
Criterion Unacceptable Minimum Satisfactory Excellent Weight
Topic and Introduction The topic has little relevancy
in the specified area and no
problem statement and the
abstract did not give any
information about what to
expect in the report.
The topic has somewhat
relevancy and/or the
problem statement was
poorly constructed,
and/or the abstract
provides little information
on the project
Relevant topic is
selected and the
problem statement is
appropriately
constructed and/or the
abstract provides
adequate information on
the project
Relevant topic is selected and
the problem statement is well
constructed and the abstract
is concise and provides
adequate information on the
project
20
Score 0 7 17 20
Writing Quality The writing is incoherent,
broken, overly long, and
contains many spelling or
grammatical errors
The writing is incoherent,
lengthy, and has some
spelling or grammar
errors
The writing is coherent,
and only has a few
spelling or grammar
errors
The writing is coherent,
concise, free of spelling errors
and grammatically correct
10
score 0 5 8 10
Technical Accuracy Work is not accurate. Work has minimal
accuracy
Work is mostly accurate
with less than two minor
errors
Work is accurate and well
constructed 30
score 0 15 26 30
Clarity of Illustrations,
Diagrams or Charts
Figures, diagrams, tables
are sloppy, and/or not
accurate, and are not
labeled.
Figures, diagrams are
not especially clear, and
but labels and diagrams
are accurate.
Figures, diagrams,
tables are clearly drawn,
clearly labeled, accurate
Figures, diagrams, tables are
clearly drawn, clearly labeled,
accurate. Labels are
descriptive. Diagrams are
exceptionally detailed.
15
Score 0 8 12 15
Solution
& Conclusion Was not logically or
effectively structured and
presents an illogical
explanation for findings.
Needs greater effort to
make it a well-
constructed paper and
the findings were not
logically presented.
Were logically organized
and made good
connections among
ideas. Presents a
logical explanation for
findings.
Information is logically and
creatively organized with
smooth transitions. Presents
a logical explanation for
findings.
25
0 15 21 25
META RUBRIC FOR LAB REPORTS
G:\Online Course Management\SBT Meta Rubrics\Lab Report Meta Rubric.xlsx
ELEC 161 – Module 2 Laboratory - Page 1
ELEC 161 Electronics II
Module 2 Lab: Ideal vs. Real Operational Amplifiers
Introduction.- In this lab we will explore the real Operational Amplifier as opposed to the Ideal Op Amp. We
will study the different parameters that make Operational Amplifiers different across models and will measure
different types of Op Am ps.
Procedure
1.- Offset Voltage
Consider a voltage amplifier with a certain gain Av. We can write Vo = Av * Vin, so when Vin =0, then we expect
the out voltage (Vout) to b.
The document discusses factors that influence voiding in solder joints, including solder paste type, stencil design, and reflow profile. Testing found that solder paste B and a 5-dot stencil pattern produced higher voiding and larger voids compared to other variables. Reflow profile also impacted voiding depending on the solder paste. Vapor phase reflow with vacuum was shown to significantly reduce voiding and could be used to rework voids. Future work will further study ways to minimize voiding, such as different solder powders, nitrogen reflow, and additional stencil designs.
Instruments basic training for iti,dme .pptSameerSutar8
The document provides information on instrument calibration training objectives including awareness of calibration, acceptance criteria, calibration methods, standards used, proper measurement techniques, instrument selection and use. It describes calibration processes and criteria for various instruments like vernier calipers, micrometers, dial indicators, bore gauges, electronic level meters, surface plates, and others. Key aspects checked include linearity, parallelism, accuracy, sensitivity, and specifications are provided for acceptance. Maintaining proper calibration is important to ensure measurement accuracy and instrument reliability.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W4 Monte Carlo Simulat...J. García - Verdugo
This document describes using Monte Carlo simulation to analyze variations in manufacturing processes and electrical circuits. It discusses generating random input variables based on their distributions, calculating output results using equations, and analyzing the output distribution to determine process capability and variation. An example simulates variations in five stacked metal parts and calculates the total dimension distribution. Another simulates variations in resistor values in an electrical circuit and calculates the distribution of the output voltage.
AN APPARATUS BASED APPROACH FOR COMPREHENSIVE MEASUREMENT OF BRIGHT BAR PARAM...IRJET Journal
The document describes the development of an apparatus for comprehensively measuring various parameters of bright bars, such as overall diameter, circularity error, straightness error, amount of bending, and number of surface cracks. The apparatus uses techniques like optical sensors, dial indicators, and UV lamps to automatically measure the parameters, providing more efficient inspection than manual methods. Test results demonstrated the machine could accurately measure bright bar parameters in less time than traditional individual instrumentation.
This document describes the design of an SWR/Wattmeter device used by ham radio operators to measure transmission strength and quality. It discusses the hardware and software design considerations including using a directional coupler to measure forward and reflected power, a microprocessor to calculate SWR and control display elements, and voltage regulation circuitry. The document also provides testing results showing the design meets constraints for accuracy across frequency ranges and power levels.
A presenation on a technique that was developed to correlate Adjecent channnel rejection of a VHF reiciever to a new method that involves a much simpler technique that can be deployed on production ATE testers
15 - Introduction to Optimization Tools Rev A.pptMohamedShabana37
This document provides an overview of TEMS Investigation and TEMS Visualization, two optimization tools from Ericsson. TEMS Investigation allows users to collect, analyze, and post-process network data to verify and optimize UMTS, GSM, GPRS, and EDGE networks. It helps troubleshoot issues like dropped calls, coverage imbalance, pilot pollution and missing neighbors. TEMS Visualization analyzes statistics from Ericsson's OSS to identify problems like missing neighbors, pilot polluters and call issues using a call event analyzer and other features. The document describes the capabilities and interface of both tools.
Scalable NDT Instruments for the Inspection of Variable Geometry ComponentsOlympus IMS
For the past several years, the aviation industry has seen above normal growth due, in part, to lower oil prices, saving major aircraft operators millions of dollars. As a result of this outstanding growth, production rates for new airplanes have increased and new aircraft programs are being launched. Consequently, aviation component manufacturers are facing new challenges including a rise in production rates, a high probability of detection (POD) due to the critical nature of the parts being manufactured, lack of skilled operators, and parts with increasingly complex geometry.
Ultrasonic phased array (PA) instruments have evolved, enabling an increase in inspection speeds and the implementation of advanced acquisition strategies. The introduction of scalable instruments and advanced acquisition strategies helps manufacturers address the inspection challenges they are facing. Scalability can now be used for nondestructive testing (NDT), enabling system integrators and manufacturers to improve the performance of their solutions by using multiple instruments in parallel. The evolution of electronic components enables advanced acquisition strategies, such as adaptive ultrasound, to be implemented. Adaptive ultrasound simplifies the inspection of complex components and improves the POD by using innovative signal-processing algorithms.
This paper presents an overview of scalable NDT instruments with the goal of helping NDT integrators and manufacturers to address the challenges they are facing in terms of system performance, production output, and quality control.
The document discusses methods for determining sample sizes in reliability testing. It covers two main approaches: the estimation approach which aims to control the confidence interval width, and the risk control approach which aims to control type I and type II errors. Examples are provided to demonstrate how to use each approach to determine the needed sample size given parameters like required reliability, confidence level, allowable failures. Both parametric and non-parametric methods are introduced for different test scenarios. Software tools can help calculate the sample sizes required to meet the test objectives.
TECNALIA presented in the European Utility Week 2014 (Amsterdam), the conference “Electromagnetic Disturbances and Blocking Problems in PLC Technologies”. Ibon Arechalde analyzed the problems with noises and impedances in PLC technologies, how TECNALIA´s EMC and Telecom Laboratory can evaluate devices in aggressive environments and how to mitigate the negative effects of noise and impedance in the electrical networks.
http://www.tecnalia.com/technological-services/
This document summarizes a digital signal processing project that involves resampling audio signals and modeling signals using autoregressive (AR) processes.
The resampling part involves downsampling two audio signals with correct and incorrect sampling rate conversions. Graphs and analysis show the resampled signals have lower quality and more distortion compared to the originals.
The AR modeling part estimates AR model coefficients from one of the signals using the Yule-Walker equations. A filter is designed to "whiten" the signal, removing noise. Graphs and audio comparison show the filtered signal has less noise but also some quality loss.
Tailor welding blanks generally used for making doors of an automobile get crack along welding line. This leads to rejection and Wrong body production. Its analysis & countermeasure shared in sides.
The document discusses an investigation into defects found in spoolshaft parts produced on machines Huffman #11 and #16. Measurements of flat widths on sample parts from both machines showed concentrations of out-of-specification measurements in certain locations around the spoolshaft. Analysis determined the measurement system was capable of precise measurements. Root cause analysis found the process variation between parts was too large, even though the process mean was within tolerance limits. Corrective action proposed lowering the lower specification limit by 20μm to reduce scrap rates given the current machine capability.
This document discusses methods for performing gauge repeatability and reproducibility (GR&R) tests on surface metrology equipment. It finds that GR&R values are often over 100% due to within-part variation rather than instrument error. Using a uniform sinusoidal specimen eliminates much of the within-part variation, resulting in more accurate GR&R values typically under 10%. The document also provides a method to determine the optimal number of measurements needed per part based on the surface variation and tolerance.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
2 Parameter vs. 3 Parameter Weibull with a Cable Flex Test
1. 2015 ARS, North America, Tucson2015 ARS, North America, Tucson
Red Room, Session #15Red Room, Session #15 Current Time:
09:40 PM
2 Parameter vs. 3 Parameter
Weibull with a Cable Flex Test
Rob Schubert, Shure Inc.
Jeff Whalen, Shure Inc.
Alexander Ho, Shure Inc.
Begins at 3:30 PM, Thursday, June 4thBegins at 3:30 PM, Thursday, June 4th
2. Rob Schubert, Shure Inc. Slide Number: 2Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
IntroductionIntroduction
Rob Schubert - Corporate Quality/Reliability Engineer at Shure Inc.
“The Most Trusted Audio Brand Worldwide”
Industry: Consumer and professional audio electronics
Founded: 1925
Products: Microphones, wireless microphone systems, headphones and
earphones, mixers, conferencing systems
All which use cables!
3. Rob Schubert, Shure Inc. Slide Number: 3Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
AgendaAgenda
Basic definitions 5 min
Shure’s cable flex test 5 min
Weibull comparison with generated data 10 min
Weibull comparison with real data which fits 3 parameter 10 min
Weibull comparison with real data which doesn’t quite fit 10 min
Advantages of each 5 min
Summary 5 min
Questions 10 min
4. Rob Schubert, Shure Inc. Slide Number: 4Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
DefinitionsDefinitions
• Shape = Slope = β : slope of the line in a probability plot
• Scale = η = shifts the data “out”
• Threshold = Location = γ : forced to zero in 2 parameter Weibull
• Anderson Darling test = How well your data fits a PDF - Smaller fits
better
• Likelihood ratio test = if small (<.05), 3 parameter fits better
5. Rob Schubert, Shure Inc. Slide Number: 5Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
DefinitionsDefinitions
Shape = Slope = β : slope of the line in a probability plot
6. Rob Schubert, Shure Inc. Slide Number: 6Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
DefinitionsDefinitions
100000100001000100
99
90
80
70
60
50
40
30
20
10
5
3
2
1
Dat a
Percent
Effect of Scale Parameter on Weibull plot
Scale = η = Shifts the data “out”
7. Rob Schubert, Shure Inc. Slide Number: 7Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
DefinitionsDefinitions
Threshold = Location = γ : forced
to zero in 2 parameter Weibull
(not represented on probability plot)
8. Rob Schubert, Shure Inc. Slide Number: 8Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Shure’s cable flex testShure’s cable flex test
Checking for continuity on 20 stations
Failure mode – open on any conductor
Each unit is independently measured
every millisecond and cycles are recorded
9. Rob Schubert, Shure Inc. Slide Number: 9Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Shure’s cable flex testShure’s cable flex test
10. Rob Schubert, Shure Inc. Slide Number: 10Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Shure’s cable flex testShure’s cable flex test
11. Rob Schubert, Shure Inc. Slide Number: 11Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Shure’s cable flex testShure’s cable flex test
Based on MIL-DTL-915G
12. Rob Schubert, Shure Inc. Slide Number: 12Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Shure’s cable flex requirementsShure’s cable flex requirements
Purpose: to ensure cables last in the field
how can we measure that?
how many samples should we use?
Originally based on 3 parameter Weibull
Later changed to 2 parameter Weibull
Now re-investigating 3 parameter Weibull
Which is correct?
13. Rob Schubert, Shure Inc. Slide Number: 13Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
100001000100
99.9
95
80
50
20
5
2
1
0.1
cable 1
Percent
100001000100
99.9
95
80
50
20
5
2
1
0.1
cable 1 - Threshold
Percent
Goodness of Fit Test
Weibull
AD = 0.999
P-Value = 0.012
3-Parameter Weibull
AD = 0.232
P-Value > 0.500
Probability Plot for cable 1
Weibull - 95% CI 3-Parameter Weibull - 95% CI
150 data points for 1 single cable (Cable #1)150 data points for 1 single cable (Cable #1)
Distribution Shape Scale Threshold L10
Weibull 2.306 9313 4050
3-Parameter 1.710 6996 1994 3946
3 parameter Weibull fits better
14. Rob Schubert, Shure Inc. Slide Number: 14Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
3 parameter Weibull – Cable #13 parameter Weibull – Cable #1
Even with 150 points, the parameters have fairly wide confidence bandsEven with 150 points, the parameters have fairly wide confidence bands
shape scale threshold
estimate 1.710 6996 1994.3
lower bound 1.484 6245 1695.1
upper bound 1.972 7836 2293.6
100001000100
99.9
99
90
80
70
60
50
40
30
20
10
5
3
2
1
0.1
cable 1 - Threshold
Percent
AD* 0.331
Shape 1.71046
Scale 6995.97
Thres 1994.34
Mean 8233.89
StDev 3756.91
Median 7640.96
I QR 5091.21
Failure 150
Censor 0
Table of Statistics
Probability Plot for cable 1
Complete Data - ML Estimates
3-Parameter Weibull - 95% CI
15. Rob Schubert, Shure Inc. Slide Number: 15Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Let’s take out the real world variability andLet’s take out the real world variability and
look at some ideal datalook at some ideal data
16. Rob Schubert, Shure Inc. Slide Number: 16Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
200 Generated points with same shape, scale and200 Generated points with same shape, scale and thresholdthreshold
100001000
99.9
95
80
50
20
5
2
1
0.1
generat ed dat a
Percent
100001000100
99.9
95
80
50
20
5
2
1
0.1
generat ed dat a - Threshold
Percent
Goodness of Fit Test
Weibull
AD = 0.510
P-Value = 0.210
3-Parameter Weibull
AD = 0.118
P-Value > 0.500
Probability Plot for generated data
Weibull - 95% CI 3-Parameter Weibull - 95% CI
Distribution Shape Scale Threshold L10
3-Parameter generated 1.710 6995 1994 -
Resulted in:
Weibull 2.436 9698 3850
3-Parameter 1.802 7428 1967 4099
17. Rob Schubert, Shure Inc. Slide Number: 17Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
3 parameter Weibull on generated data3 parameter Weibull on generated data
100001000100
99.9
99
90
80
70
60
50
40
30
20
10
5
3
2
1
0.1
generat ed dat a - Threshold
Percent
AD* 0.179
Shape 1.80269
Scale 7428.49
Thres 1967.72
Mean 8573.30
StDev 3792.23
Median 8029.53
I QR 5182.39
Failure 200
Censor 0
Table of Statistics
Probability Plot for generated data
Complete Data - ML Estimates
3-Parameter Weibull - 95% CI
shape scale threshold
estimate 1.80 7428 1967
lower bound 1.55 6617 1483
upper bound 2.10 8339 2452
18. Rob Schubert, Shure Inc. Slide Number: 18Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Let’s take sets of this data:Let’s take sets of this data:
ETC.
ETC.
Then randomize…
Continue until 1000 sets are made:
3 samples, 5 samples, 10 samples, 20 samples and 40 samples
All in all, 78000 data points analyzed per cable
Then randomize…
19. Rob Schubert, Shure Inc. Slide Number: 19Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Let’s first look at Anderson DarlingLet’s first look at Anderson Darling
20. Rob Schubert, Shure Inc. Slide Number: 20Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling by sample sizeAnderson Darling by sample size
(Statistically this is ±3-5%)
21. Rob Schubert, Shure Inc. Slide Number: 21Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Distribution of differences in theDistribution of differences in the
Anderson Darling statisticAnderson Darling statistic
3 parameter
lower
2 parameter
lower
Setsofdata
22. Rob Schubert, Shure Inc. Slide Number: 22Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Let’s review the effect of sampleLet’s review the effect of sample
size on thresholdsize on threshold
23. Rob Schubert, Shure Inc. Slide Number: 23Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold by sample sizeThreshold by sample size
(Statistically this is ±3-5%)
24. Rob Schubert, Shure Inc. Slide Number: 24Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Distribution of differences in ThresholdDistribution of differences in ThresholdSetsofdata
25. Rob Schubert, Shure Inc. Slide Number: 25Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
What about confidence intervals ofWhat about confidence intervals of
those thresholds?those thresholds?
26. Rob Schubert, Shure Inc. Slide Number: 26Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
40 samples – Threshold confidence intervals40 samples – Threshold confidence intervals
As threshold increases, confidence interval goes down
40003000200010000
16000
14000
12000
10000
8000
6000
4000
2000
0
40 samples t hreshold
40sampleconfrange
27. Rob Schubert, Shure Inc. Slide Number: 27Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
What about slope? Let’s reviewWhat about slope? Let’s review
slopeslope
28. Rob Schubert, Shure Inc. Slide Number: 28Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Difference in slope (2 vs 3 parameter)Difference in slope (2 vs 3 parameter)
2 param.
Slope
steeper
3 param.
Slope
steeper
Setsofdata
29. Rob Schubert, Shure Inc. Slide Number: 29Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
1000 different generated Weibull sets of 40 samples1000 different generated Weibull sets of 40 samples
with different slope, scale and thresholdwith different slope, scale and threshold
50.037.525.012.50.0-12.5-25.0
Median
Mean
5.55.04.54.03.53.0
1st Quartile 1.7403
Median 3.4172
3rd Quartile 6.3986
Maximum 48.5880
4.6248 5.3782
3.1649 3.6993
5.7641 6.2974
A-Squared 68.33
P-Value < 0.005
Mean 5.0015
StDev 6.0189
Variance 36.2270
Skew ness 1.6762
Kurtosis 10.9488
N 983
Minimum -32.8531
Anderson-Darling Normality Test
95% Confidence I nterv al for Mean
95% Confidence I nterv al for Median
95% Confidence I nterv al for StDev
9 5 % Conf idence I nt er v als
Summary for difference
2 param.
Slope
steeper
3 param.
Slope
steeper
2 parameter slope is generally steeper
30. Rob Schubert, Shure Inc. Slide Number: 30Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Do not ignore the WarningsDo not ignore the Warnings
* WARNING * Variance/Covariance matrix of estimated parameters
does not exist. The threshold parameter is assumed fixed when
calculating confidence intervals.
* WARNING * Convergence has not been reached for the log-
likelihood criterion.
* WARNING * Newton-Raphson algorithm has not converged after
20 iterations.
* WARNING * Convergence has not been reached for the parameter
estimates criterion.
40 samples 20 samples 10 samples 5 samples 3 samples
Warnings 3 131 1193 countless countless
31. Rob Schubert, Shure Inc. Slide Number: 31Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Back to real data! Cable #1Back to real data! Cable #1
32. Rob Schubert, Shure Inc. Slide Number: 32Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
3 parameter Weibull – Cable #13 parameter Weibull – Cable #1
shape scale threshold
Estimate 1.710 6996 1994.3
lower bound 1.484 6245 1695.1
upper bound 1.972 7836 2293.6
100001000100
99.9
99
90
80
70
60
50
40
30
20
10
5
3
2
1
0.1
cable 1 - Threshold
Percent
AD* 0.331
Shape 1.71046
Scale 6995.97
Thres 1994.34
Mean 8233.89
StDev 3756.91
Median 7640.96
I QR 5091.21
Failure 150
Censor 0
Table of Statistics
Probability Plot for cable 1
Complete Data - ML Estimates
3-Parameter Weibull - 95% CI
33. Rob Schubert, Shure Inc. Slide Number: 33Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling by sample size (Cable #1)Anderson Darling by sample size (Cable #1)
34. Rob Schubert, Shure Inc. Slide Number: 34Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling – compared to generatedAnderson Darling – compared to generated
Does not fit 2 parameterDoes not fit 2 parameter
Cable 1 = real data
35. Rob Schubert, Shure Inc. Slide Number: 35Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling – compared to generatedAnderson Darling – compared to generated
Does not fit 3 parameterDoes not fit 3 parameter
Cable 1 = real data
O
rder reversed
for clarity
36. Rob Schubert, Shure Inc. Slide Number: 36Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling – compared to generatedAnderson Darling – compared to generated
Likelihood ratio testLikelihood ratio test
37. Rob Schubert, Shure Inc. Slide Number: 37Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling – compared to generatedAnderson Darling – compared to generated
3 parameter scores lower than 2 parameter3 parameter scores lower than 2 parameter
38. Rob Schubert, Shure Inc. Slide Number: 38Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold by sample size (Cable #1)Threshold by sample size (Cable #1)
39. Rob Schubert, Shure Inc. Slide Number: 39Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold – compared to generated:Threshold – compared to generated:
Above upper confidence limitAbove upper confidence limit
Cable 1 = real data
40. Rob Schubert, Shure Inc. Slide Number: 40Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold – compared to generated:Threshold – compared to generated:
Below lower confidence limit (and positive)Below lower confidence limit (and positive)
Cable 1 = real data
41. Rob Schubert, Shure Inc. Slide Number: 41Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold – compared to generated:Threshold – compared to generated:
NegativeNegative
Cable 1 = real data
O
rder reversed
for clarity
42. Rob Schubert, Shure Inc. Slide Number: 42Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold – compared to generated:Threshold – compared to generated:
Within confidence limitsWithin confidence limits
Cable 1 = real data
43. Rob Schubert, Shure Inc. Slide Number: 43Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Median of positive results - Delta toMedian of positive results - Delta to
“true”“true” (150 – 200 data points)(150 – 200 data points)
O
rder reversed
for clarity
44. Rob Schubert, Shure Inc. Slide Number: 44Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Let’s try a cable that doesn’t quite fitLet’s try a cable that doesn’t quite fit
either Weibulleither Weibull
45. Rob Schubert, Shure Inc. Slide Number: 45Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
100001000
99.9
95
80
50
20
5
2
1
0.1
cable 2
Percent
1000100
99.9
95
80
50
20
5
2
1
0.1
cable 2 - Threshold
Percent
Goodness of Fit Test
Weibull
AD = 3.961
P-Value < 0.010
3-Parameter Weibull
AD = 0.940
P-Value = 0.019
Probability Plot for cable 2
Weibull - 95% CI 3-Parameter Weibull - 95% CI
170 data points for Cable #2170 data points for Cable #2
46. Rob Schubert, Shure Inc. Slide Number: 46Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
3 parameter Weibull – cable #23 parameter Weibull – cable #2
shape scale threshold
estimate 2.00 965 968.9
lower bound 1.73 864 906.9
upper bound 2.31 1078 1030.8
1000100
99.9
99
90
80
70
60
50
40
30
20
10
5
3
2
1
0.1
Cable 2 - Threshold
Percent
AD* 1.034
Shape 1.99766
Scale 965.093
Thres 968.853
Mean 1824.16
StDev 447.561
Median 1772.17
I QR 619.268
Failure 170
Censor 0
Table of Statistics
Probability Plot for Cable 2
Complete Data - ML Estimates
3-Parameter Weibull - 95% CI
47. Rob Schubert, Shure Inc. Slide Number: 47Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling by sample sizeAnderson Darling by sample size
48. Rob Schubert, Shure Inc. Slide Number: 48Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling – compare to previous:Anderson Darling – compare to previous:
Does not fit 2 parameterDoes not fit 2 parameter
Cable 1 & 2 = real data
49. Rob Schubert, Shure Inc. Slide Number: 49Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling – compare to previous:Anderson Darling – compare to previous:
Does not fit 3 parameterDoes not fit 3 parameter
O
rder reversed
for clarity
Cable 1 & 2 = real data
50. Rob Schubert, Shure Inc. Slide Number: 50Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling – compare to previous:Anderson Darling – compare to previous:
Likelihood ratio testLikelihood ratio test
Cable 1 & 2 = real data
51. Rob Schubert, Shure Inc. Slide Number: 51Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Anderson Darling – compare to previous:Anderson Darling – compare to previous:
3 parameter scores lower than 2 parameter3 parameter scores lower than 2 parameter
Cable 1 & 2 = real data
52. Rob Schubert, Shure Inc. Slide Number: 52Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold by sample size (Cable #2)Threshold by sample size (Cable #2)
53. Rob Schubert, Shure Inc. Slide Number: 53Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold – compare to generated:Threshold – compare to generated:
Above upper confidence limitAbove upper confidence limit
54. Rob Schubert, Shure Inc. Slide Number: 54Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold – compare to generated:Threshold – compare to generated:
Below lower confidence limitBelow lower confidence limit
O
rder reversed
for clarity
55. Rob Schubert, Shure Inc. Slide Number: 55Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold – compare to generated:Threshold – compare to generated:
NegativeNegative
O
rder reversed
for clarity
56. Rob Schubert, Shure Inc. Slide Number: 56Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Threshold – compare to generated:Threshold – compare to generated:
Within Confidence limitsWithin Confidence limits
57. Rob Schubert, Shure Inc. Slide Number: 57Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Median of positive results - Delta to “true”Median of positive results - Delta to “true”
(150 – 200 data points)(150 – 200 data points)
negative
58. Rob Schubert, Shure Inc. Slide Number: 58Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Advantages of the 2 parameterAdvantages of the 2 parameter
WeibullWeibull
Works with a very small set of data
Negative threshold doesn’t occur (As
seen, negative threshold makes little
sense)
Simpler to understand for reliability
engineers, most common distribution used
(little need to discuss)
59. Rob Schubert, Shure Inc. Slide Number: 59Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Advantages of the 3 parameterAdvantages of the 3 parameter
WeibullWeibull
Simple specification setting (can set
threshold as a minimum life)
May be more accurate, but needs
significant data to actualize
60. Rob Schubert, Shure Inc. Slide Number: 60Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
““Schubert’s Six” rulesSchubert’s Six” rules
3 parameter vs 2 parameter3 parameter vs 2 parameter
3 and 5 samples are unusable (10 is barely
acceptable) with 3 parameter.
3 parameter slope is less steep.
Negative threshold means you should take more
samples.
Positive threshold generally measures high, but
asymptotically approaches the true answer with
more samples.
Confidence interval of threshold is smaller as
threshold gets larger.
Do not ignore the *Warnings*!
61. Rob Schubert, Shure Inc. Slide Number: 61Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Shure’s directionShure’s direction
Specify minimum flex life and test via 3
parameter Weibull (all cases, regardless
of fit)
Set specifications by use case with a 25%
safety factor
Test with a minimum of 20 samples, retest
(add data) if threshold is negative or if
calculation shows warnings
62. Rob Schubert, Shure Inc. Slide Number: 62Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Where to Get More InformationWhere to Get More Information
MIL-DTL-915G
www.shure.com (product line)
Presentation, Data, Macros, etc. available here:
https://www.dropbox.com/sh/5qyncmbshmc4af1/AAD-EZcTF0jvcPxBQZUB-UCua?dl=0
63. Rob Schubert, Shure Inc. Slide Number: 63Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
Authors/ContributorsAuthors/Contributors
Rob Schubert
Corporate Quality/Reliability Engineer
Shure Inc – 8 years
Schubert_Rob@shure.com
Certified Reliability Engineer (ASQ)
Master’s in Acoustical Engineering, Penn State
Thesis: Use of Multiple-Input Single-Output Methods to Increase Reliability of
Measurements of Road Noise in Automobiles
Previous work experience: Ford (13 years) - Quality/Reliability Engineer, 6 Sigma
Black belt, Noise & Vibration Engineer
Contributors
64. Rob Schubert, Shure Inc. Slide Number: 64Session #15Red Room
AppliedReliabilitySymposium,NorthAmerica2015
QuestionsQuestions
Thank you for your attention.
Do you have any questions?