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.
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.
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.
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 3. Failure Time Distributions
1.Constant failure rate distributions
2.Increasing failure rate distributions
3.Decreasing failure rate distributions
4.Weibull Analysis – Why use Weibull?
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
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.
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 3. Failure Time Distributions
1.Constant failure rate distributions
2.Increasing failure rate distributions
3.Decreasing failure rate distributions
4.Weibull Analysis – Why use Weibull?
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
Applications of the PMP. Cell Formation in Group TechnologySSA KPI
AACIMP 2010 Summer School lecture by Dmitry Krushinsky. "Applied Mathematics" stream. "The p-Median Problem and Its Applications" course. Part 5.
More info at http://summerschool.ssa.org.ua
Electronics Reliability Prediction Using the Product Bill of MaterialsCheryl Tulkoff
Common MTBF Misconceptions
It is difficult to represent field failures with calculated MTBF models.
It is important for consumers to know how MTBFs were generated
and what the limitations are for those
calculations.
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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
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.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
4. Outline
• MTBF – calculation
• MTBF – a very poor four letter acronym
• History of Use
• It’s Misleading
• A better measure
• Actually, we’ve been talking about MTTF
9. Other Issues
• Time – just because it is hours…
• Between – note the duration of the failure
free period!
• Failure – use the customer definition
10. History of Use
• Early Parts Count based on adding failure
rates of components (60’s and early 70’s)
− λ1t − λ 2t − λnt
R(t ) = e •e •• e
− ( λ1 + λ 2 ++ λ n ) t
R(t ) = e
11. History of Use
• Remember Slide Rule and Mechanical
Adding Machines
• Victor Adding Machine
12. Beta = 0.63
Depth Cut Response data
Weibull Probability Plot
.5 Weibull Distribution ML Fit
Exponential Distribution ML Fit
.3
95% Pointwise Confidence Intervals
.2
.1
.05
.03
Fraction Failing
.02
.01
.005
.003
.001
.0005
.0003
.0002
.0001
10^-01 10^00 10^01 10^02 10^03 10^04
DEPTH.CUT
13. Beta = 1.97
test7.df data
Weibull Probability Plot
.7 Weibull Distribution ML Fit
.3 Exponential Distribution ML Fit
95% Pointwise Confidence Intervals
.1
.03
.01
.003
Fraction Failing
.001
.0003
.0001
.00003
.00001
.000003
.000001
.0000003
.0000001
.00000003
.00000001
1 10 100 1000 10000 100000
Depth In
14. Use Reliability
• R(t) is the probability that a random unit
drawn from the population will still be
operating by t hours
• R(t) is the fraction of all units in the
population that will survive t hours
Applied Reliability, 2nd Ed., pg 29
15. The four (five) elements
• Function
• Duration
• Probability
• Environment
• They all change over time
16. Use better models/distributions
−( t ) β
• Weibull RWeibull (t ) = e η
• Type I Gumbel
− ( et )
• Exponential RGumbel (t ) = e
• Log Normal − λt
Rexp onential (t ) = e
• Etc. t
ln T
50
Rlog normal (t ) = Φ
σ
17. Other Measures
• What is the cost of a field failure?
• Warranty $ per unit shipped
• Returns/field failure $ per unit shipped
• What else could you use?
18. Actually…
• MTBF is or should be used for repairable
systems
• MTTF is what I’ve been talking about
• MTTF is calculated the same as MTBF when we
assume
– negligible repair time
– Interarrival times as from an independent sample of
nonrepairable parts
– Expontential distribution for lifetime of parts
• See Chap 10, Applied Reliability for more info
21. Where to Get More Information
• Tobias, Paul A. and Trindade, David C.,
Applied Reliability, 2nd Ed. Chapman &
Hall, New York, 1995.
• “The Limitations of Using the MTTF as a
Reliability Specification” Reliability Edge,
Qtr 2, 2000, Vol 1, Issue 1.
22. Presenter’s Biographical Sketch
• Fred Schenkelberg, Consultant
• Independent Reliability Engineering and
Management Consultant for past 5 years.
Previously at HP Corporate Reliability
Engineering Program for 5 years.
• MS Statistics Stanford, BS Physics USMA
• fms@opsalacarte.com
• (408) 710-8248
• www.opsalacarte.com
Editor's Notes
1000 started 1/100 chance of failing each hour Remainng units times same chance of failure for each hour to determine how many are left.