A draft version of the paper that was eventually published as “J.A.Jones & J.A.Hayes, ”A comparison of electronic-reliability prediction models”, IEEE Transactions on reliability, June 1999, Volume 48, Number 2, pp 127-134”
Provide with the kind permission of the author, J.A.Jones
This is a presentation to the top management as to why reliability is important and what is the difference between a maintenance engineer and a reliability engineer.
Paper on the issues with mtbf published in the Spring 2011 issue of the RMSP Journal.
MTBF is widely used to describe the reliability of a component or system. It is also often misunderstood and used incorrectly. In some sense, the very name “mean time between failures” contributes to the misunderstanding. The objective of this paper is to explore the nature of the MTBF misunderstandings and the impact on decision-making and program costs.
Mean-Time-Between-Failure (MTBF) as defined by MIL-STD-721C Definition of Terms for Reliability and Maintainability, 12 June 1981, is
A basic measure of reliability for repairable items: The mean number of life units during which all parts of the item perform within their specified limits, during a particular measurement interval under stated conditions.
The related measure, Mean-Time-To-Failure (MTTF) is define as
A basic measure of reliability for non-repairable items: The total number of life units of an item divided by the total number of failures within that population, during a particular measurement interval under stated conditions.
System reliability and types of systems in machine designVikasSuroshe
This presentation gives brief description about, What is system reliability, types of systems. The points discussed are: System, Calculating Reliability Factor for System, System Configurations Types, Series Configuration, Parallel Configuration (Redundant System), Mixed Configuration (Combine Series-Parallel System), Reliability Block Diagram, Reliability Considerations, Advantages and Disadvantages of various configurations etc.
This is a presentation to the top management as to why reliability is important and what is the difference between a maintenance engineer and a reliability engineer.
Paper on the issues with mtbf published in the Spring 2011 issue of the RMSP Journal.
MTBF is widely used to describe the reliability of a component or system. It is also often misunderstood and used incorrectly. In some sense, the very name “mean time between failures” contributes to the misunderstanding. The objective of this paper is to explore the nature of the MTBF misunderstandings and the impact on decision-making and program costs.
Mean-Time-Between-Failure (MTBF) as defined by MIL-STD-721C Definition of Terms for Reliability and Maintainability, 12 June 1981, is
A basic measure of reliability for repairable items: The mean number of life units during which all parts of the item perform within their specified limits, during a particular measurement interval under stated conditions.
The related measure, Mean-Time-To-Failure (MTTF) is define as
A basic measure of reliability for non-repairable items: The total number of life units of an item divided by the total number of failures within that population, during a particular measurement interval under stated conditions.
System reliability and types of systems in machine designVikasSuroshe
This presentation gives brief description about, What is system reliability, types of systems. The points discussed are: System, Calculating Reliability Factor for System, System Configurations Types, Series Configuration, Parallel Configuration (Redundant System), Mixed Configuration (Combine Series-Parallel System), Reliability Block Diagram, Reliability Considerations, Advantages and Disadvantages of various configurations etc.
Mechanical Reliability Prediction: A Different ApproachHCL Technologies
This paper critically analyses the current industry practices for making reliability prediction prevalent among the aircraft manufacturers and further explores the more accurate and cost effective methods for predicting the failure rate of a component or subsystem during the early design phase of the product development cycle namely NSWC method , PoF approach and SSI theory. It elucidates the effectiveness of these alternative approaches with the help of a case study on Hydraulic Accumulator (HYDAC).
Andrew Rowland's short paper on why MTBF may not be a good choice.
See www.nomtbf.com for more material on the perils of MTBF, and what to do about it.
Application of Lifetime Models in Maintenance (Case Study: Thermal Electricit...iosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Parameter Estimation of Software Reliability Growth Models Using Simulated An...Editor IJCATR
The parameter estimation of Goel’s Okomotu Model is performed victimisation simulated annealing. The Goel’s Okomotu
Model is predicated on Exponential model and could be a easy non-homogeneous Poisson method (NHPP) model. Simulated
annealing could be a heuristic optimisation technique that provides a method to flee local optima. The information set is optimized
using simulated annealing technique. SA could be a random algorithmic program with higher performance than Genetic algorithmic
program (GA) that depends on the specification of the neighbourhood structure of a state area and parameter settings for its cooling
schedule.
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 2. Reliability Calculations
1.Use of failure data
2.Density functions
3.Reliability function
4.Hazard and failure rates
Objectives
To understand Weibull distribution
To be able to use Weibull plot for failure time analysis and
diagnosis
To be able to use software to do data analysis
Organization
Distribution model
Parameter estimation
Regression analysis
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
Anomaly or Fault Detection
One or more monitored parameters has departed a “normal” operating envelope.
Change can be related to some degradation in the machine.
Otherwise may be anomaly (unknown) or sensor problem
Fault Isolation or Diagnosis
A statement of the nature of a condition made after observing symptoms or indicators.
Localize the problem to the component level of repair.
Identification of the most probable root cause or failure mode.
Assessment of current severity.
These last three can really help in prognostics if we know how to use them.
Estimation of Reliability Indices of Two Component Identical System in the Pr...IJLT EMAS
Progress in science & technology has made
engineering systems more powerful than ever. The intensity of
sophistication in high-tech industrial producers emerged with
reliability problems. Therefore the problem of reliability
continue to exist and more likely to require complex solutions.
Consequently, the field of reliability analysis and statistical
probability modeling of the systems and components were
growing. Ever since the theory of reliability was formally
recognized statistical and modeling of the components/ systems
analysis was used to develop various reliability measures that are
important to assess the system performance. In this research
paper, an attempt is made to find an approach of estimation
method, which could establish a formal estimation procedure to
estimate the reliability measures and also developed estimates of
the system reliability indices practically under the influence of
common cause shock failures as well as intrinsic failures. From
the results, it is seen that maximum likelihood approach used
was found useful in the estimation process to find estimate for
the reliability measures of the system, where small sample is
essential point of interest in the case of reliability analysis. The
estimates so derived using empirical procedure do possess the
property that MSE in each case is well within the prescribed
error, i.e. coincides even to the three decimal places are more.
Mechanical Reliability Prediction: A Different ApproachHCL Technologies
This paper critically analyses the current industry practices for making reliability prediction prevalent among the aircraft manufacturers and further explores the more accurate and cost effective methods for predicting the failure rate of a component or subsystem during the early design phase of the product development cycle namely NSWC method , PoF approach and SSI theory. It elucidates the effectiveness of these alternative approaches with the help of a case study on Hydraulic Accumulator (HYDAC).
Andrew Rowland's short paper on why MTBF may not be a good choice.
See www.nomtbf.com for more material on the perils of MTBF, and what to do about it.
Application of Lifetime Models in Maintenance (Case Study: Thermal Electricit...iosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Parameter Estimation of Software Reliability Growth Models Using Simulated An...Editor IJCATR
The parameter estimation of Goel’s Okomotu Model is performed victimisation simulated annealing. The Goel’s Okomotu
Model is predicated on Exponential model and could be a easy non-homogeneous Poisson method (NHPP) model. Simulated
annealing could be a heuristic optimisation technique that provides a method to flee local optima. The information set is optimized
using simulated annealing technique. SA could be a random algorithmic program with higher performance than Genetic algorithmic
program (GA) that depends on the specification of the neighbourhood structure of a state area and parameter settings for its cooling
schedule.
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 2. Reliability Calculations
1.Use of failure data
2.Density functions
3.Reliability function
4.Hazard and failure rates
Objectives
To understand Weibull distribution
To be able to use Weibull plot for failure time analysis and
diagnosis
To be able to use software to do data analysis
Organization
Distribution model
Parameter estimation
Regression analysis
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
Anomaly or Fault Detection
One or more monitored parameters has departed a “normal” operating envelope.
Change can be related to some degradation in the machine.
Otherwise may be anomaly (unknown) or sensor problem
Fault Isolation or Diagnosis
A statement of the nature of a condition made after observing symptoms or indicators.
Localize the problem to the component level of repair.
Identification of the most probable root cause or failure mode.
Assessment of current severity.
These last three can really help in prognostics if we know how to use them.
Estimation of Reliability Indices of Two Component Identical System in the Pr...IJLT EMAS
Progress in science & technology has made
engineering systems more powerful than ever. The intensity of
sophistication in high-tech industrial producers emerged with
reliability problems. Therefore the problem of reliability
continue to exist and more likely to require complex solutions.
Consequently, the field of reliability analysis and statistical
probability modeling of the systems and components were
growing. Ever since the theory of reliability was formally
recognized statistical and modeling of the components/ systems
analysis was used to develop various reliability measures that are
important to assess the system performance. In this research
paper, an attempt is made to find an approach of estimation
method, which could establish a formal estimation procedure to
estimate the reliability measures and also developed estimates of
the system reliability indices practically under the influence of
common cause shock failures as well as intrinsic failures. From
the results, it is seen that maximum likelihood approach used
was found useful in the estimation process to find estimate for
the reliability measures of the system, where small sample is
essential point of interest in the case of reliability analysis. The
estimates so derived using empirical procedure do possess the
property that MSE in each case is well within the prescribed
error, i.e. coincides even to the three decimal places are more.
Robust Fault-Tolerant Training Strategy Using Neural Network to Perform Funct...Eswar Publications
This paper is intended to introduce an efficient as well as robust training mechanism for a neural network which can be used for testing the functionality of software. The traditional setup of neural network architecture is used constituting the two phases -training phase and evaluation phase. The input test cases are to be trained in first phase and consequently they behave like normal test cases to predict the output as untrained test cases. The test oracle measures the deviation between the outputs of untrained test cases with trained test cases and authorizes a final decision. Our framework can be applied to systems where number of test cases outnumbers the
functionalities or the system under test is too complex. It can also be applied to the test case development when the modules of a system become tedious after modification.
Reliability Prediction of Port Harcourt Electricity Distribution Network Usin...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Decentralized data fusion approach is one in which features are extracted and processed individually and finally fused to obtain global estimates. The paper presents decentralized data fusion algorithm using factor analysis model. Factor analysis is a statistical method used to study the effect and interdependence of various factors within a system. The proposed algorithm fuses accelerometer and gyroscope data in an inertial measurement unit (IMU). Simulations are carried out on Matlab platform to illustrate the algorithm.
Estimation of Weekly Reference Evapotranspiration using Linear Regression and...IDES Editor
The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
Expert system design for elastic scattering neutrons optical model using bpnnijcsa
In present paper, a proposed expert system is designed to obtain a trained formulae for the optical model
parameters used in elastic scattering neutrons of light nuclei for (7Li), at energy range between [(1) to
(20)] MeV. A simple algorithm has used to design this expert system, while a multi-layer backwardpropagation
neural network (BPNN) is applied for training and testing the data used in this model. This
group of formulae may get a simple expert system occurring from governing formulae model, and predicts
the critical parameters usually resulted from the complicated computer coding methods. This expert system
may use in nuclear reactions yields in both fission and fusion nature who gives more closely results to the
real model.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Initial Optimal Parameters of Artificial Neural Network and Support Vector Re...IJECEIAES
This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬ -insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.
RCM is a process used to identify what Preventive Maintenance or Condition Based Maintenance you need to implement so you get the Reliability you need from your equipment.
Doing Reliability Centered Maintenance (RCM) helps us take care of our equipment. And, taking care of our equipment is very much like taking care of ourselves.
An overview of the basic process to create an ALT using one of 6 different approaches. Slides used for presentation to the ASQ Silicon Valley evening meeting on Nov 15th 2017.
We work on projects to improve reliability. There may not be the field data immediately available. Let’s explore what you can do to improve the overall program while delivering on your project. Specifically, what’s with cost and procurement?
Detailed Information: As a reliability professional we often work with a team focused on improving the reliability of single product or system. We work with the resources and capabilities of the organization. For me a reliability project is one product or line, a program is the entire organization and lifecycle. We bring specific tools and knowledge, yet rely on the overall reliability culture of an organization to be successful
The overall reliability program may or may not have the field data, root cause analysis and other element of information that allow us to effectively solve problems for a specific project. In some cases we have to work to improve the overall program while striving to create a reliable product. Let’s explore what you should do when you are building a reliability model for a new project and would like to use previous reliability history.
If the data is not available what do you do? What are your options? Let’s discuss what happens when the procurement team consistently selects the least expensive and least reliable components. What are your options? You can and should change the way entire departments do business, for the good of the project and the organization. Let’s discuss the scope of your role as a reliability engineer.
This Accendo Reliability webinar originally broadcast on 19 May 2015.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Draft comparison of electronic reliability prediction methodologies
1. A Comparison of Electronic Reliability Prediction Methodologies
J.A.Jones and J.A.Hayes
International Electronics Reliability Institute
Department of Electronic and Electrical Engineering
Loughborough University of Technology
Leicestershire, LE11 3TU, United Kingdom
Tel: 01509-222897 Fax: 01509-222854
1. SUMMARY AND CONCLUSIONS
One of the most controversial techniques used at present in the field of reliability is the use of
reliability prediction techniques based on component failure data for the estimation of system
failure rates. The International Electronics Reliability Institute (IERI) at Loughborough
University are in an unique position. Over a number of years a large amount of reliability
information has been collected from leading British and Danish electronic manufacturing
companies. This data is of such high quality that IERI are able to perform the comparison
exercise with a number of boards of different types.
A number of boards were selected from the IERI field failure database and their reliability
was predicted and compared with the actual observed performance in the field. The prediction
techniques were based on the MIL-217E, HRD4, Siemens (SN29500), CNET, and Bellcore
(TR-TSY-000332) models. For each method the associated published failure rates were used.
Hence parts count analyses were performed on a number of boards from the database and
these were compared with the failure rate observed in the field. The prediction values were
seen to differ greatly from the observed field behaviour and from each other. Further analysis
showed that each method of prediction was sensitive to widely different physical parameters.
This suggests that predictions obtained by the different models can't be compared and that
great care should be taken when selecting a prediction technique since the physical
parameters of the system will affect the prediction obtained in possibly unforeseen ways.
2. INTRODUCTION
Reliability prediction often effects major decisions in system design. It is based on the
assumption that systems fail as a result of failures of component parts, and those parts fail
partly as a result of exposure to application stress[1]. This means that by some consideration
of the structure of such a piece of equipment and by further consideration of its usage it is
possible to obtain an estimate of the systems reliability in that particular application.
There are many reasons why this task may be necessary. These include feasibility evaluation
where the compatibility of a design concept is weighed against the design reliability
requirements for acceptance, and design comparison where different parts of a system can be
compared and any necessary trade off such as cost, reliability, weight etc. can be made.
Further uses are for the identification of potential reliability problems and as a reliability input
into other tasks such as maintainability analysis, testability evaluations and FMECA. [2]
1
2. Electronic failure prediction methodology (EFPM) is normally carried out in two stages. The
first stage is known as parts count analysis and requires comparatively little information about
the system. This method is generally used early in the design phase to obtain a preliminary
estimate of the system reliability. The second stage is known as parts stress analysis and
involves detailed knowledge about the system but can, in principle, provide a more realistic
estimate of the reliability. The parts stress method tends to be used towards the end of the
design cycle when actual circuit parameters have been established.
Rightly or wrongly reliability prediction methods are widely accepted throughout the
electronics industry. These methods are often used as a yardstick for the comparison of
different equipment. However, many manufacturers have commented that the models can be
wildly inaccurate when compared with the performance in the field, particularly in the case of
the observed failure rates of modern microelectronic devices, and their use can lead to
increased costs and complexity while deluding engineers into following a flawed set of
perceptions and leaving truly effective reliability improvement measures unrecognised[1].
In general the telecommunication industry models offer improved accuracy when compared
to other models due primarily to the quantity and quality of in house data on mature
equipment but tend to be less accurate for newer designs and for similar systems produced by
other industries. The main advantage of the MIL-217 approach is its wide acceptance
throughout the defence industry. This enables it to be used as a general yardstick which
allows comparison between different equipment. However it has many shortcomings witch
have been the subject of much discussion on recent years.
IERI at Loughborough University are in an unique position. A collaborative exercise
involving a number of British and Danish electronic systems manufacturers has resulted in
the establishment of a database containing high quality data at the system, board and
component level. This has enabled IERI to carry out predictions on a large number of boards
and to compare these with observed performance in the field. The initial investigation
involved parts count analysis only and It should be stated that some of the predictions
described in this paper were performed using older versions of some of the handbooks that
have more recently been updated. This means that although the principles of the prediction
remain the same the actual figures obtained from these now obsolete handbooks may differ
from those obtained from the new, updated versions.
3. THE THEORY OF RELIABILITY PREDICTION
A electronic system can be considered to be a network of components all interconnected to
one another in various complex ways. This real life model is however unsuitable for
reliability analysis since it is far too complex. In order to study the reliability of systems a
number of assumptions need to be made.
1) Any component failure causes a system failure.
This is normally modelled as a series configuration (or chain structure). This is the simplest
of the many models available and as such is the most widely used for reliability modelling of
systems. A series configuration of n items will have a reliability function defined by (1)
2
3. R(t) = P( x 1 , x 2 , ... , X n ) = P( x 1)P( x 2 | x 1)P( x 3 | x 1 x 2) ... P( x n | x 1 x 2 ... x n-1) (1)
where P(x1) etc. Is the probability that item X1 will fail and P(x2| x1) etc. is the conditional
probability that X2 will fail given X1 has failed.
2) The components that make up the system must be independent, This means that a failure by
a single component must not affect other components in the system.
If the n items x1,x2, ... ,xn are independent, then
n
R(t) = P( x 1)P( x 2)... P( x n ) = p( x )
i=1
i (2)
This suggests that assuming that no component failure affects any other then the reliability of
a system made up of these components can simply be calculated by multiplying the
probability of failure for each component in the system together. However in a general system
this can be difficult since each component’s probability of failure could be a complex
function. If however simple functions are used then it is possible to proceed further.
3) The component failure behaviour must be governed by a constant-hazard model
This last assumptions means that if the component model is e- i t then equation (2) becomes
n
n (- i t )
R(t) =
i=1
e- i t = e i=1
(3)
This equation is the not only the most commonly used and the most elementary system
reliability formula it is also the most commonly abused. It should be remembered that in order
for this equation to be valid then the condition given in clause 3 above must be met. There is
ample evidence to suggest that this is not in fact the case [4] but data handbook suppliers and
users still assume that this is true. Recently methodologies have been developed [5] that will
allow prediction of system reliability but do not make this assumption.
4. OVERVIEW OF PREDICTION METHODOLOGIES
In most of the available prediction methodology handbooks the equation for the reliability of
a system given in equation (3) is modified by the addition of different multipliers, called
factors. These factors relate to parameters which can effect the overall reliability of a
system such as environment, stress level, temperature etc. In general the equation for system
failure rate , given the failure rate of the constituent components, will be
3
4. n m
SS = E . Gi F .Ni (4)
i=1 k 1 k
where Gi is generic failure rate for the i'th device, Fk is the stress factor multiplier for the
k'th stress type for the i'th device, N i is the quantity of i'th device type, and E is the
environment factor for the system.
Each model used in this paper uses an equation similar to (4) and is described briefly below.
4.1 BELLCORE
This method is defined in [6] and was developed by Bell communications research for use by
the electronics industry so that they would be aware of Bellcore’s' view of the requirements of
reliability prediction procedures for electronic equipment’s. The data presented is based upon
field data, laboratory tests, MIL-HDBK-217E, device manufacturers data, unit suppliers data
or engineering analysis. The factors used in this model take into account the variations in
equipment operating environment, quality and device application conditions such as device
temperature and electrical stress level.
4.2 CNET
This method is defined in [7] and was developed by French National Centre Of
Telecommunications. The data used comes from the analysis of breakdowns of the
equipment used by the military and civil Administration, and other equipment and component
manufacturers. This handbook forms a common base intended to make reliability predictions
uniform in France. The factors used in this model take into account the variations in
equipment operating environment, quality and device temperature. It should be noted that the
method used in this paper is the 'simplified method' so called because it comes from a British
Telecom translation of the important parts of the actual CNET handbook.
4.3 HRD4
This method is defined in [8] and was developed by British Telecom Materials and
Components Centre for use by designers and users of electronic equipment so that there exists
a common basis exists for system reliability prediction. The generic failure rates given in the
handbook are estimates of the upper 60% confidence levels, based upon, wherever possible,
data collected from the in service performance of the equipment installed in the UK inland
telecommunications network. Where such data is not available for particular components,
alternative sources or estimated values have been used and the status of the source indicated
by a letter code. The factors used in this model take into account the variations in
equipment operating environment, quality and device temperature.
4.4 MIL-217
Mil-Hdbk-217[9] was developed by the US Department of Defence with the assistance of the
military departments, federal agencies, and industry for use by the electronic manufacturers
supplying to the military. The handbook describes two methods, namely parts count and parts
stress which are used to predict the reliability of electronic components, systems, or
subsystems in different stages of the design. The failure rates given in the handbook have in
4
5. the main derived from test bed and accelerated life studies. The factors used in this model
take into account the variations in equipment operating environment, and device quality.
4.5 SIEMENS
This method is defined in [10] and was developed by Siemens AG for the use of Siemens and
Siemens associates as a uniform basis for reliability prediction. The standard presented in the
document is based on failure rates under specified conditions. The failure rates given were
determined from application and testing experience taking external sources (e.g. Mil-Hdbk-
217) into consideration. Components are categorised into many different groups each of
which has a slightly different reliability model. The factors used in this model take into
account the variations in device operating temperature and electrical stress.
5. ANALYSIS OF PREDICTION METHODOLOGIES
Six different board designs were selected from the IERI database. These designs were chosen
such that they contained a wide range of component types and were from several different
applications. Table 1 describes the environments and applications of the boards.
Table 1:Brief description of the circuit boards
BOARD ENVIRONMENT APPLICATION
1 Ground Mobile Radio System
2 Ground Mobile Radio System
3 Ground Benign Telephone Exchange
4 Ground Benign Telephone Exchange
5 Ground Mobile Command System
6 Ground Mobile Command System
The analysis of two of the boards will be given in detail to show the methodology and to
illustrate how the models can be used for parts provisioning purposes.
5.1 CIRCUIT BOARD ONE
The first board design chosen from the IERI database contained 338 components with six
different component types. The board came from a system used in a high quality radio
application. Table 2 contains the predicted failure rate for each component type used on the
board. Each failure rate is given in FITs and is the failure contribution for each component
type on the board taking into consideration environment, component numbers etc.
Table 2:Contribution to failure rate by each component type for circuit board one.
DEVICE BELLCORE CNET HRD4 MIL-217 SIEMENS
Ceramic Multilayer Capacitor 210 45 3 4 35
pn-Junction Diode 125 300 300 9 25
Bipolar Digital IC 936 390 168 50 60
Metal Oxide Resistor 1590 596 51 52 795
Discrete Bipolar Transistor 7812 20937 4000 1225 1250
Tantalum Electrolytic Capacitor 1350 3780 40 594 180
Total 12023 26048 4562 1934 2345
5
6. As can be seen from the totals row in Table 2 the reliability predicted for this board differs
widely between the different models. The actual field behaviour of this board is summarised
in Table 3 which shows the actual numbers of operating hours observed, the total number of
failure in this time and the calculated failure rate with the upper and lower 95% 2 confidence
limits.
Table 3:Actual field behaviour for circuit board one.
Number of failures 19
Total number of operating hours 4444696
FIT rate 4274
Upper 95% 2 confidence limit 6400
Lower 95% 2 confidence limit 2572
Deviation from observed value
25,000
21,774
20,000
15,000
10,000
7,749
5,000
288
0
-1,929
-2,340
-5,000
Bellcore CNET HRD4 MIL-217 Siemens
Prediction Methodology
Figure 1:Deviation of predicted reliability of board one from observed value using different models
The differences from the various predicted values to the actual field figures are summarised
in Figure 1. It can be seen that some of the models gave predictions that were optimistic
(MIL-217 and Siemens) whereas others give pessimistic predictions. Table 4 shows the
percentage contribution to failure of each component on the circuit board. It is found that for
each prediction model that the most likely cause of failure was the bipolar transistor which
has the largest percentage contribution to failure in each case.
Table 4:Largest percentage contributions to failure for circuit board one.
PREDICTION LARGEST NEXT LARGEST CONTRIBUTION
MODEL CONTRIBUTION
Bellcore Bipolar Transistor (65%) Metal Oxide Resistor (13%)
CNET Bipolar Transistor (80%) Tantalum Electrolytic Capacitor (14%)
HRD4 Bipolar Transistor (87%) pn-Junction Diode (6.5%)
MIL-217 Bipolar Transistor (63%) Tantalum Electrolytic Capacitor (30%)
Siemens Bipolar Transistor (53%) Metal Oxide Resistor (33%)
6
7. Analysis of the field information shows that the bipolar transistor was the main cause of
failure of this circuit board in the field, and so spare provisioning using any of the available
models would have proved accurate.
5.2 CIRCUIT BOARD TWO
The second board chosen from the IERI database was slightly more complex board containing
149 components with eighteen different component types. The board comes from a system
used in a telecommunication application. Table 5 contains the predicted failure rate in FITs
for each component type used on the board.
Table 5:Contribution to failure rate by each component type for circuit board two.
DEVICE BELLCORE CNET HRD4 MIL-217 SIEMENS
Transformer 3 9 7 6 5
Coil activated relay 770 605 440 1430 88
Aluminium electrolytic capacitor 210 22 120 16 120
Polyester capacitor 17 4 6 1 14
pn-Junction diode 230 149 345 152 230
Zener diode 16 63 87 94 350
LED 9 15 280 65 0
Bipolar digital IC (11-100 gates) 59 413 7 3 20
Bipolar linear IC (1-10 transistors) 14 57 13 3 500
Bipolar linear IC (11-100 transistors) 42 80 13 3 150
MOS digital IC (1-10 gates) 83 903 27 3 40
Rectangular connector 7 1 50 8 22
Varistor 6 0 10 0 10
Carbon film resistor 182 27 10 11 26
Wire-wound resistor 127 42 6 1 30
Metal film resistor 7 25 0.5 0.5 2
Rocker switch 5 44 30 1 20
Bipolar transistor 25 19 16 38 20
TOTAL 1812 2478 1467.5 1835.5 1647
As can be seen from the totals row the reliability predicted for this board also differs widely
between the different methods. The actual field behaviour of this board is summarised in
Table 6 and the difference between the predicted values and the observed field value is shown
in Figure 2
Table 6:Actual field behaviour for circuit board two.
Number of failures 5
Total number of operating hours 8.5x106
FIT rate 587
Upper 95% 2 confidence limit 1202
Lower 95% 2 confidence limit 190.6
7
8. Deviation from observed value
2,500
2,000
1,891
1,500
1,225 1,248
1,060
1,000
880
500
0
Bellcore CNET HRD4 MIL-217 Siemens
Prediction Methodology
Figure 2:Deviation of predicted reliability from observed values for
different models
Notice in this case all predictions are pessimistic. The percentage contribution to failure of
each component on the circuit board is shown in Table 7.
Table 7:Largest percentage contributions to failure for circuit board two.
PREDICTION LARGEST CONTRIBUTION NEXT LARGEST
MODEL CONTRIBUTION
Bellcore Coil Activated Relay (42%) pn-Junction Diode (12%)
CNET MOS Digital IC with 1-10 gates (36%) Coil Activated Relay (24%)
HRD4 Coil Activated Relay (30%) pn-Junction Diode (23%)
MIL-217 Coil Activated Relay (77%) pn-Junction Diode (8%)
SIEMENS Bipolar Linear IC with 1-10 transistors (30%) Zener Diode (21%)
Field observation showed all the failures on this board to be caused by coil activated relays
and bipolar transistors with the relay causing one more failure than the transistor. If parts
provisioning had been done according to two of the models, then the incorrect part would
have been identified.
The reliability of the rest of the boards used in this study were calculated using all the
aforementioned models. Figure 3 shows the percentage deviation from the observed field
failure rate for the six board designs selected from the IERI-CORD database. The predictions
not only differ widely between the various models but they also differ greatly from the
observed field failure rate. The models are not always consistent in the deviation from this
observed field value, they can be optimistic in some cases while pessimistic in others. his
suggests that there are some underlying factors that are causing divergence of the models.
8
9. Board type
Board one
Board two
Board three
Board four
Board five
Board six
-200 -100 0 100 200 300 400 500 600
% deviation from field value
BellCore CNET HRD4 MIL-217E Siemens
Figure 3:Percentage deviation from the observed field value for six
boards
In order to investigate this apparent inconsistency in the various models it is necessary to look
at each model carefully and analyse the way in which the predicted failure rate is influenced
by the various parameters that influence system performance. This technique is known as
sensitivity analysis and it is useful when examining a model for major dependencies.
If the various prediction models are sensitive to similar parameters then the inconsistency
shown above must come from the nature of the underlying data. If the models are sensitive to
different parameters then this suggests that different models of failure were used when the
models were derived. Hence this makes it impossible to use base failure rates from one
model in another and also makes it imperative to only use in house data derived in the same
manner as the chosen model when extending the coverage of the model to such things as
custom components. This makes it much more difficult for companies performing predictions
to use data gathered in house.
6. SENSITIVITY ANALYSIS OF PREDICTION METHODOLOGIES
This is done by varying temperature, quality, stress, and environment in turn while keeping
the others at typical or nominal values. The results are presented graphically and show
percentage variation of predicted failure rate from the nominal value while a single parameter
is varied within the limits that the models allow. The largest spread shows the parameter that
has the greatest effect on the model's prediction. Care should be taken in some cases where
the effect is accentuated by a highly non-linear variation in one of the parameters' factors.
This is particularly true when the parameters are discrete, as in the case of environment,
where selection of a particular environment can cause a large change in the associated
factor. The degree of non-linearity can be demonstrated using graphs of normalised stress
versus deviation where normalised stress is defined as the ratio between values of the range of
available factors and the nominal factor value for the parameter under investigation.
9
10. 6.1 BELLCORE MODEL
The percentage deviation in the board failure rate from the nominal with respect to different
stress levels using the Bellcore methodology is shown in Figure 4.
200
Percentage deviation from nominal
150 90%
100
65ºC
Low
50
0 GM
30ºC
High
-50
10%
GB
-100
Temperature Environment
Electrical Stress Quality
Model parameter
Figure 4:Sensitivity of predicted value for the Bellcore
methodology
As can be seen Figure 4 the allowed variation in the electrical stress makes the largest
difference to the calculated failure rate.
200
Percentage Deviation from nominal
150
100
50
0
Temperature
(50)
Electrical Stress
(100)
0 0.5 1 1.5 2
Normalised Stress
Figure 5:Variation in predicted value for the Bellcore methodology
with changes in temperature and electrical stress.
The non-linear nature of the effect of varying electrical stress and temperature are shown in
Figure 5. This shows that the BELLCORE model is based upon an Arrhenius style
acceleration formula which is reflected in this non-linearity.
6.2 CNET MODEL
Figure 6 shows the percentage deviation in the board failure rates with respect to different
stress levels using the CNET(simplified) methodology. As can be seen from Figure 6, the
range in quality factor has the largest influence on the calculated failure rates.
10
11. 1,000
Percentage deviation from nominal
Low
800
600
400
200
ML
70ºC
0
40ºC High
SL
(200)
Temperature Environment
Electrical Stress Quality
Model parameter
Figure 6:Sensitivity of predicted value for the CNET
methodology
The quality factor used in this model is based upon a set of discrete quality bands over which
the factor is defined. This means that the quality factor is a stepwise non-linear function of
the device quality.
6.3 HRD4 MODEL
The percentage deviation in the board failure rates with respect to different stress levels using
the HRD4 methodology is shown in Figure 7.
150
Percentage deviation from nominal
100 150ºC Low
50
0 GM
<70ºC
(50)
High
(100) GB
Temperature Environment
Electrical Stress Quality
Model parameter
Figure 7:Sensitivity of predicted value for the HRD4
methodology
As can be seen from Figure 7, it is the allowed range of quality factors that makes the largest
difference to the calculated failure rates. The quality factor used in this model is based upon a
set of discrete quality bands over which the factor is defined. Again this means that the
quality factor is a stepwise non-linear function of the device quality.
6.4 MIL-217 MODEL
Figure 8 shows the percentage deviation in the board failure rates with respect to different
stress levels using the MIL-217E methodology. The figure shows that it is the allowed
11
12. variation in environment factors that causes the largest difference in the calculated failure
rates.
5000
Percentage deviation from nominal
CL
4000
3000
2000
1000
Low
0
GB High
-1000
Temperature Environment
Electrical Stress Quality
Model parameter
Figure 8: Sensitivity of predicted value for the MIL-217
methodology
However this effect is enhanced because of the large difference caused by the cannon launch
(CL) environment as is apparent from Figure 9.. If the CL environment is omitted then the
quality would become the predominant factor.
5000
Percentage deviation from nominal
4000
3000
2000
1000
0
-1000
GB GF MP NS NH ARW AIT AIA AUC AUB AUF MFF USL CL
GMS GM NSB NU NUU AIC AIB AIF AUT AUA SF MFA ML
Environment
Figure 9:The effect of MIL-217 Environments on the predicted
value
The quality factor used in this model is based upon a set of discrete quality bands over which
the factor is defined. This means that the quality factor is a stepwise non-linear function of
the device quality.
6.5 SIEMENS MODEL
The percentage deviation in the board failure rates with respect to different stress levels using
the Siemens methodology is shown in Figure 10. As can be seen from the figure it is the
range of temperature factor that causes the largest difference in the calculated failure rates.
12
13. The non-linear nature of the electrical stress and temperature effects are shown in Figure 11
This
5000
Percentage deviation from nominal
4000 130ºC
3000
2000
1000
100%
0
40ºC 10%
-1000
Temperature Environment
Electrical Stress Quality
Model parameter
Figure 10:Sensitivity of predicted value for the Siemens methodology
means that the Siemens model is based upon an Arrhenius style acceleration formula which is
reflected by this non-linearity.
5000
Percentage deviation from nominal
4000
3000
2000
1000
Electrical Stress
0
Temperature
-1000
0 0.5 1 1.5 2 2.5 3 3.5
Normalised Stress
Figure 11:Variation in predicted value for the Siemens methodology as
temperature and electrical stress are varied.
7. CONCLUSIONS
The results are summarised in Table 7.It is evident that some of the models are more sensitive
to a factor that varies according to an Arrhenius model, such as temperature and electrical
13
14. stress, while others are more sensitive to the discrete factors used to model environment
and quality.
Table 7: Most sensitive parameter in each prediction model
Prediction Model Greatest sensitivity
Bellcore Electrical Stress
CNET(Simplified) Quality
HRD4 Quality
MIL-217E Environment, Quality
Siemens Temperature
It is not surprising that direct comparisons of the models result in wide variations. Although
the models are based upon the same criteria there is disagreement about the effects the
different parameters have on the failure rate.
Also by carefully examining the models, it was observed that although the quality levels are
clearly defined within each procedure, it is extremely difficult to find a quality level
description that is compatible across all models. In addition every organisation has developed
their model according to the experience they have obtained in the field and have tailored the
model to meet their specific needs.
It is inadvisable to compare the models' prediction with field performance unless the systems
used are manufactured according to the guidelines and procedures that are specified by the
model designers. Under such circumstances the system manufacturers would argue that their
models are suitable for reliability prediction.
Care must also be taken when comparing the predicted reliability with that observed in the
field as prediction models assume a constant hazard rate. It has been observed however that
early life failures do occur in the field even after system burn-in [4]
Even if the above considerations are taken into account, there is no guarantee that the field
reliability will be the same as that predicted. This is due to the underlying reason that models
are generally simple empirical approximations. Indeed it is postulated that because of this,
their use should actively be discouraged. Moreover, they do not take into account many of
the other critical factors such as vibrations, mechanical shock, etc.
14
15. 8. REFERENCES
1. C. T. Leonard and M. Pecht, "How failure prediction methodology affect electronic equipment design",
Quality and Reliability Engineering International, Vol. 6, 1990.pp 243-249,
2. S. F. Morris, "Use and application of MIL-HDBK-217", Solid State Technology 1990. August, pp 65-69,.
3. D. S. Campbell and J. A. Hayes, "The organisation of a study of the field failure of electronic components",
Quality and Reliability Engineering International, Vol. 3, , 1987, pp 251-258.
4. D. S. Campbell, J. A. Hayes, J. A. Jones & A. P. Schwarzenberger, "Reliability Behaviour of Electronic
Components As a Function of Time", Quality and Reliability Engineering International, Vol. 8, , 1992, pp 161-
166.
5 J Møltoft, "Reliability Engineering Based on Field Information - The Way Ahead", Quality and Reliability
Engineering International, Vol. 10, 1994, pp 399-409.
6. Bellcore Technical Reference TR-TSY-000332, "Reliability Prediction Procedure for Electronic
Equipment", Issue 2, July 1988.
7. Centre National D'Etudes des Telecommunications, "Recueil de Donnees de Fiabilite du CNET",1983 edition.
8. British Telecom, "Handbook of Reliability Data for Components used in Telecommunications Systems", Issue
4, January 1987.
9. US Mil-Hdbk-217,"Reliability Prediction of Electronic Equipment", Version E, October 1986.
10. Siemens AG, SN29500, "Reliability and Quality Specifications Failure Rates of Components", Siemens
Technical Liaison and Standardisation 1986.
15
16. AUTHORS
Mr Jeff. A. Jones
Jeff Jones has been a research fellow at Loughborough University since it the reliability work began in 1984 .
He has a first degree in electronic engineering and physics and has since obtained a masters degree by research
(MPhil) entitled "The Implementation of a Field Reliability Database". He is currently the principal British
expert and deputy convenor on IEC/TC56 WG 7 Component Reliability and alternate British expert on CECC
SC/56x, Dependability. He is an active member within the UK dependability community He is a chartered
physicist, a chartered engineer and a member of the IEEE.
Mr J.A.Jones , IERI, Department of Electronic and Electrical Engineering, Loughborough University,
Loughborough, Leics., LE11 3TU, United Kingdom
Tel: +44 1509 222897 Email : J.A.Jones@Lboro.ac.uk
Mr Joe. A. Hayes
Mr Joe Hayes is a lecturer within the department of Electronic and Electrical Engineering at Loughborough
University. He has a degree in physics from Newcastle University and has a masters degree in semiconductor
physics. He has been involved with the reliability work at Loughborough since it started and has played a major
role in the development of the International Electronics Reliability Institute.. He is a chartered physicist and a
member of the institute of physics
Mr J.A.Hayes, Department of Electronic and Electrical Engineering, Loughborough University,
Loughborough, Leics., LE11 3TU, United Kingdom
Tel: +44 1509 2222851 Email : J.A.Hayes@Lboro.ac.uk
16