Practical Reliability Engineering 5th Edition by Patrick D. T. O’Connor, ISBN-13: 978-0470979815


Practical Reliability Engineering 5th Edition by Patrick D. T. O’Connor, ISBN-13: 978-0470979815

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  • Publisher: ‎ Wiley; 5th edition (January 30, 2012)
  • Language: ‎ English
  • 512 pages
  • ISBN-10: ‎ 047097981X
  • ISBN-13: ‎ 978-0470979815

Fully revised edition of the best-selling book, that presents a good balance of reliability mathematics, engineering methods and practices, providing an up-to-date overview for engineering students and reliability engineers alike.

With emphasis on practical aspects of engineering, this bestseller has gained worldwide recognition through progressive editions as the essential reliability textbook. This fifth edition retains the unique balanced mixture of reliability theory and applications, thoroughly updated with the latest industry best practices.

Practical Reliability Engineering fulfills the requirements of the Certified Reliability Engineer curriculum of the American Society for Quality (ASQ). Each chapter is supported by practice questions, and a solutions manual is available to course tutors via the companion website.

Enhanced coverage of mathematics of reliability, physics of failure, graphical and software methods of failure data analysis, reliability prediction and modelling, design for reliability and safety as well as management and economics of reliability programmes ensures continued relevance to all quality assurance and reliability courses.

Notable additions include:

  • New chapters on applications of Monte Carlo simulation methods and reliability demonstration methods.
  • Software applications of statistical methods, including probability plotting and a wider use of common software tools.
  • More detailed descriptions of reliability prediction methods.
  • Comprehensive treatment of accelerated test data analysis and warranty data analysis.
  • Revised and expanded end-of-chapter tutorial sections to advance students’ practical knowledge.

The fifth edition will appeal to a wide range of readers from college students to seasoned engineering professionals involved in the design, development, manufacture and maintenance of reliable engineering products and systems.

Table of Contents:

Front Matter
Preface to the First Edition
Preface to the Second Edition
Preface to the Third Edition
Preface to the Third Edition Revised
Preface to the Fourth Edition
Preface to the Fifth Edition
1 Introduction to Reliability Engineering
1.1 What is Reliability Engineering?
1.2 Why Teach Reliability Engineering?
Figure 1.1 Perception of risk.
1.3 Why Do Engineering Products Fail?
Figure 1.2 Load–strength – discrete values.
Figure 1.3 Load–strength – distributed values.
Figure 1.4 Load–strength – interfering distributions.
Figure 1.5 Time-dependent load and strength variation.
1.4 Probabilistic Reliability
1.5 Repairable and Non-Repairable Items
1.6 The Pattern of Failures with Time (Non-Repairable Items)
Figure 1.6 The ‘bathtub’ curve.
1.7 The Pattern of Failures with Time (Repairable Items)
1.8 The Development of Reliability Engineering
1.9 Courses, Conferences and Literature
1.10 Organizations Involved in Reliability Work
1.11 Reliability as an Effectiveness Parameter
1.12 Reliability Programme Activities
1.13 Reliability Economics and Management
Figure 1.7 Reliability and life cycle costs (traditional view).
Figure 1.8 Reliability/Quality and life cycle costs.
Figure 1.9 Reliability and life cycle costs (practical applications).
Periodic Publications
2 Reliability Mathematics
2.1 Introduction
2.2 Variation
2.2.1 A Cautionary Note
2.3 Probability Concepts
Figure 2.1 Samples with defectives (black squares).
2.4 Rules of Probability
Figure 2.2 Dual redundant system.
Example 2.1
Example 2.2
Example 2.3
Example 2.4
2.5 Continuous Variation
Figure 2.3 (a) Frequency histogram of a random sample, (b) frequency histogram of another random sample from the same population, (c) data of many samples shown with measurement intervals of 0.5.
Figure 2.4 Continuous probability distribution.
Figure 2.5 Measures of central tendency.
2.5.1 Measures of Central Tendency
2.5.2 Spread of a Distribution
2.5.3 The Cumulative Distribution Function
Figure 2.6 Typical cumulative distribution function (cdf).
2.5.4 Reliability and Hazard Functions
2.5.5 Calculating Reliability Using Microsoft Excel® Functions
Figure 2.7 Probability Density Function (pdf) and its application to reliability.
2.6 Continuous Distribution Functions
2.6.1 The Normal (or Gaussian) Distribution
Figure 2.8 The normal (Gaussian) distribution.
Example 2.5
Figure 2.9 (a) The pdf f(x) versus x; (b) the cdf F(x) versus x (see Example 2.5).
2.6.2 The Lognormal Distribution
2.6.3 The Exponential Distribution
2.6.4 The Gamma Distribution
2.6.5 The χ2 Distribution
2.6.6 The Weibull Distribution
2.6.7 The Extreme Value Distributions
Table 2.1 Sample data taken randomly from a common population.
Figure 2.10 Extreme value distributions. Extreme Value Type I Extreme Value Type II Extreme Value Type III The Extreme Value Distributions Related to Load and Strength
2.7 Summary of Continuous Statistical Distributions
2.8 Variation in Engineering
Figure 2.11 Shapes of common failure distributions, reliability and hazard rate functions (shown in relation to t).
2.8.1 Is the Variation Normal?
Figure 2.12 Curtailed normal distribution.
Figure 2.13 Effect of selection.
Figure 2.14 Skewed distribution.
Figure 2.15 Bi-modal distribution.
Figure 2.16 Four distributions with the same means and SDs.
2.8.2 Effects and Causes
2.8.3 Tails
2.9 Conclusions
2.10 Discrete Variation
2.10.1 The Binomial Distribution
Example 2.6
Example 2.7
2.10.2 The Poisson Distribution
Example 2.8
Example 2.9
2.11 Statistical Confidence
2.11.1 Confidence Limits on Continuous Variables
Example 2.10
Figure 2.17 Utilizing Excel’s Goal Seek to find Z-value corresponding to the 95% confidence interval.
Figure 2.18 Confidence levels for normal distribution.
2.12 Statistical Hypothesis Testing
2.12.1 Tests for Differences in Means (z Test)
Example 2.11
Example 2.12
2.12.2 Use of the z Test for Binomial Trials
Example 2.13
Table 2.2 Results for tests in Example 2.13.
2.12.3 χ2 Test for Significance
Example 2.14
2.12.4 Tests for Differences in Variances. Variance Ratio Test (F Test)
Table 2.3 Life test data on two items.
Example 2.15
2.13 Non-Parametric Inferential Methods
Table 2.4 Critical values of r for the sign test. Reproduced by permission of McGraw-Hill.
2.13.1 Comparison of Median Values The Sign Test
Example 2.16 The Weighted Sign Test Tests for Variance Reliability Estimates
2.14 Goodness of Fit
2.14.1 The χ2 Goodness-of-Fit Test
Table 2.5 Data from an overstress life test of transistors.
Example 2.17
2.14.2 The Kolmogorov–Smirnov Test
Table 2.6 Failure data with ranked values of xi.
Example 2.18
2.15 Series of Events (Point Processes)
2.15.1 Trend Analysis (Time Series Analysis)
Figure 2.19 Arrival and interarrival values.
Example 2.19
2.15.2 Superimposed Processes
Figure 2.20 Rate of occurrence for superimposed processes.
2.16 Computer Software for Statistics
2.17 Practical Conclusions
Helpful introductory sources
More advanced works
3 Life Data Analysis and Probability Plotting
3.1 Introduction
3.1.1 General Approach to Life Data Analysis and Probability Plotting
3.1.2 Statistical Data Analysis Methods
Figure 3.1 Probability plotting alternatives in regards to the possible pdf of failure distribution.
3.2 Life Data Classification
Figure 3.2 Normal probability plot.
3.2.1 Complete Data
3.2.2 Censored Data
3.2.3 Right Censored (Suspended)
Figure 3.3 Complete data set.
Figure 3.4 Right censored data.
3.2.4 Interval Censored
Figure 3.5 Interval censored data.
Figure 3.6 Left censored data.
3.2.5 Left Censored
3.3 Ranking of Data
3.3.1 Concept of Ranking
3.3.2 Mean Rank
3.3.3 Median Rank
3.3.4 Cumulative Binomial Method for Median Ranks
Table 3.1 Median rank for the sample size of 5.
3.3.5 Algebraic Approximation of the Median Rank
3.3.6 Ranking Censored Data
3.4 Weibull Distribution
3.4.1 Two Parameter Weibull
3.4.2 Weibull Parameter Estimation and Probability Plotting
Figure 3.7 Weibull probability paper. Abscissa – ln t, Ordinate – ln ln
Example 3.1 Weibull Analysis using Rank Regression
Figure 3.8 Data plotted on Weibull paper for Example 3.1, β ≈ 2.0 and η ≈ 320.
Example 3.2 Calculating Adjusted Ranks
Table 3.2 Data summary and adjusted ranks calculation for Example 3.2.
3.4.3 Three Parameter Weibull
Figure 3.9 3-parameter Weibull distribution plotted with Weibull++®.
3.4.4 The Relationship of β-Parameter to Failure Rates and Bathtub Curve
Figure 3.10 Relationship between the bathtub curve and the Weibull slope β.
3.4.5 BX-Life
3.5 Computerized Data Analysis and Probability Plotting
3.5.1 Rank Regression on X
Figure 3.11 Minimizing distance in the X-direction.
3.5.2 Maximum Likelihood Estimation (MLE)
Example 3.3 Illustrating MLE Method on Exponential distribution
3.5.3 Recommendation on Using Rank Regression vs. MLE
Figure 3.12 Two-sided 90% confidence bounds.
3.6 Confidence Bounds for Life Data Analysis
Figure 3.13 One-sided confidence bounds.
Table 3.3 5 and 95% ranks for the sample size of 5.
3.6.1 Confidence Intervals for Weibull Data
3.6.2 Individual Parameter Bounds
Figure 3.14 Weibull++® two-sided 90% confidence bounds for Weibull distribution. Fisher Matrix Bounds Likelihood Ratio Bounds Beta Binomial Bounds Monte Carlo Confidence Bounds Bayesian Confidence Bounds
Example 3.4 Manual Calculation of Confidence Bounds on the Weibull Parameter β
3.6.3 Alternative Methods for Calculating Confidence Bounds
Figure 3.15 Confidence limits for shape parameter β for different confidence values.
3.7 Choosing the Best Distribution and Assessing the Results
3.7.1 Goodness of a Distribution Fit
Figure 3.16 Weibull++® 90% confidence bounds on B10-life and Reliability (Example 3.1).
Figure 3.17 Weibull++® distribution ranking based on the goodness of fit (Rank Regression on X).
3.7.2 Mixed Distributions
Figure 3.18 Separate groups of data points.
Figure 3.19 Mixed Weibull distribution plotted with Weibull++®.
3.7.3 Engineering Approach to Finding Best Distribution
Example 3.5 Breaking Strength of a Wire
Table 3.4 Breaking strengths of 15 samples of wire of equal length.
Figure 3.20 Probability plot of the breaking strength (Weibull++®), Extreme value distribution.
3.8 Conclusions
4 Monte Carlo Simulation
4.1 Introduction
4.2 Monte Carlo Simulation Basics
4.3 Additional Statistical Distributions
Figure 4.1 Simplified Monte Carlo simulation procedure with y = f(x1, x2, xn).
4.3.1 Uniform Distribution
4.3.2 Triangular Distribution
Figure 4.2 (a) Rectangular and (b) Triangular distributions.
4.4 Sampling a Statistical Distribution
4.4.1 Generating Random Variables Using Excel Functions
Table 4.1 Statistical distributions sampling using Microsoft Excel®.
4.4.2 Number of Simulation Runs and the Accuracy of Results
Example 4.1
4.5 Basic Steps for Performing a Monte Carlo Simulation
Example 4.2 Calculating the Probability of Exceeding Yield Strength
Figure 4.3 Monte Carlo simulation process.
Figure 4.4 Monte Carlo Simulation using Microsoft Excel®.
4.6 Monte Carlo Method Summary
Table 4.2 Input variables generated by @Risk® for Example 4.2 (Reproduced by permission of Palisade Corporation).
Figure 4.5 Simulation results including the histogram and the best fit distribution for Example 4.2 using @Risk v.5.7.
Figure 4.6 Monte Carlo Simulation sensitivity analysis by @Risk®.
5 Load–Strength Interference
5.1 Introduction
5.2 Distributed Load and Strength
Figure 5.1 Distributed load and strength: (a) non-overlapping distributions, (b) overlapping distributions.
Figure 5.2 Effect of safety margin and loading roughness. Load L’ causes failure of a proportion of items indicated by the shaded area.
Figure 5.3 Truncation of strength distribution by screening.
5.3 Analysis of Load–Strength Interference
5.3.1 Normally Distributed Strength and Load
Example 5.1
5.3.2 Other Distributions of Load and Strength
5.4 Effect of Safety Margin and Loading Roughness on Reliability (Multiple Load Applications)
Figure 5.4 Failure probability–safety margin curves when both load and strength are normally distributed (for large n and n = 1) (Carter, 1997).
Figure 5.5 Characteristic regions of a typical failure probability–safety margin curve (Carter, 1997).
Figure 5.6 Failure probability–safety margin curves for asymmetric distributions (loading roughness = 0.3) (Carter, 1997).
Figure 5.7 Failure probability–safety margin curves for asymmetric distributions (loading roughness = 0.9) (Carter, 1997).
Example 5.2 (electronic)
Figure 5.8 Load data (sampled at 10 s intervals).
Table 5.1 Mean ranking of load test data.
Table 5.2 Failure data for 100 transistors.
Example 5.3 (mechanical fatigue)
Table 5.3 Maximum loads vs. percentages of the users applying those loads.
Table 5.4 Washing machine loads vs. percent of motors failing at 100 cycles.
Figure 5.9 Load-Strength distribution chart generated with Weibull++® for Example 5.3.
5.5 Practical Aspects
6 Reliability Prediction and Modelling
6.1 Introduction
6.2 Fundamental Limitations of Reliability Prediction
6.3 Standards Based Reliability Prediction
6.3.1 MIL-HDBK-217
6.3.2 Telcordia SR-332 (Formerly Bellcore)
6.3.3 IEC 62380 (Formerly RDF 2000)
6.3.4 NSWC-06/LE10
6.3.5 PRISM and 217Plus
6.3.6 China 299B (GJB/z 299B)
6.3.7 Other Standards
6.3.8 IEEE Standard 1413
6.3.9 Software Tools for Reliability Prediction
6.4 Other Methods for Reliability Predictions
6.4.1 Field Return Based Methods
6.4.2 Fusion of Field Data and Reliability Prediction Standards
6.4.3 Physics of Failure Methods
6.4.4 ‘Top Down’ Approach to Reliability Prediction
6.5 Practical Aspects
6.6 Systems Reliability Models
6.6.1 The Basic Series Reliability Model
Figure 6.1 Series System.
6.6.2 Active Redundancy
Figure 6.2 Dual redundant system.
6.6.3 m-out-of-n Redundancy
6.6.4 Standby Redundancy
Figure 6.3 Reliability block diagram for a missile system.
6.6.5 Further Redundancy Considerations
6.7 Availability of Repairable Systems
Figure 6.4 (a) Non-repairable system and (b) Repairable system.
Table 6.1 Reliability and availability for some systems configurations. (R. H. Myers, K. L. Wong and H. M. Gordy, Reliability Engineering for Electronic Systems, Copyright © 1964 John Wiley & Sons, Inc. Reprinted by permission of John Wiley & Sons, Inc.)
Example 6.1
Table 6.2 MTBR and replacement costs for the four modules.
Table 6.3 Cost per year of replacing the modules.
6.8 Modular Design
Example 6.2
6.9 Block Diagram Analysis
Figure 6.5 Block diagram decomposition.
Example 6.3
6.9.1 Cut and Tie Sets
Figure 6.6 (a) Cut sets and (b) tie sets.
Example 6.4
6.9.2 Common Mode Failures
Figure 6.7 Effect of common mode failure.
6.9.3 Enabling Events
6.9.4 Practical Aspects
6.10 Fault Tree Analysis (FTA)
Figure 6.8 Standard symbols used in fault tree analysis.
6.11 State-Space Analysis (Markov Analysis)
Figure 6.9 Reliability block diagram of engine.
Figure 6.10 FTA for engine (incomplete).
Example 6.5
Figure 6.11 Two-state Markov state transition diagram.
Figure 6.12 Tree diagram for Example 6.5.
6.11.1 Complex Systems
Figure 6.13 Transient availability of repaired system.
6.11.2 Continuous Markov Processes
Figure 6.14 State–space diagram for a single-component repairable system.
6.11.3 Limitations, Advantages and Applications of Markov Analysis
6.12 Petri Nets
Figure 6.15 Basic structures of logic relations for Petri nets.
6.12.1 Transformation between Fault Trees and Petri Nets
Figure 6.16 A fault tree.
Figure 6.17 Correlations between fault tree and Petri net.
Figure 6.18 The Petri net transformation of Figure 6.16.
6.12.2 Minimum Cut Sets
Figure 6.19 Minimum cut sets of Figure 6.18.
Figure 6.20 The absorption principle of equivalent Petri nets.
6.12.3 Marking Transfer
Example 6.6
Figure 6.21 Petri net diagram for an airbag controller with a detection system (Kleyner and Volovoi, 2008).
6.13 Reliability Apportionment
6.14 Conclusions
Figure 6.22 System XYZ block diagram.
7 Design for Reliability
7.1 Introduction
Figure 7.1 Cost of design change.
7.2 Design for Reliability Process
Figure 7.2 Design for reliability (DfR) activities flow.
7.3 Identify
7.3.1 Benchmarking
7.3.2 Environments
7.3.3 Environment Distribution
Example 7.1
Figure 7.3 Statistical distribution of the annual driving distances (per passenger car in Europe).
7.3.4 Quality Function Deployment (QFD)
Figure 7.4 Quality function deployment for electric motor design.
7.3.5 Programme Risk Assessment
7.4 Design
7.4.1 Computer-Aided Engineering
7.4.2 Failure Modes, Effects and Criticality Analysis (FMECA)
7.4.3 Steps in Performing an FMECA
Figure 7.5 FMEA worksheet for AIAG-3 method.
Figure 7.6 MIL-STD-1629 Method 102 worksheet for criticality analysis.
7.4.4 Uses for FMECA
7.4.5 FMECA Software Tools
Figure 7.7 Part of output listing from CARE®, the FMECA software.
7.4.6 Reliability Predictions for FMECA
7.4.7 Load-Strength Analysis
7.4.8 Hazard and Operability Study (HAZOPS)
Table 7.1 Load–strength analysis example.
7.4.9 Parts, Materials and Processes (PMP) Review
7.4.10 Non-Material Failure Modes
Table 7.2 HAZOPS on motion system (partial).
7.4.11 Critical Items List
7.4.12 Load Protection
7.4.13 Protection against Strength Degradation
7.4.14 Design Reviews
7.4.15 Design Review Based on Failure Modes (DRBFM)
7.4.16 Human Reliability
7.5 Analyse
7.5.1 Field Return and Warranty Data Analysis
7.6 Verify
7.6.1 Degradation Analysis
7.6.2 Configuration Control
7.7 Validate
7.8 Control
7.8.1 Design Analysis for Processes
7.8.2 Variation
Figure 7.8 Shaft–bore interference.
7.8.3 Process FMECA
7.8.4 ‘Poka Yoke’
7.8.5 Testability Analysis
7.8.6 Test Yield Analysis
7.8.7 Maintainability Analysis
7.9 Assessing the DfR Capability of an Organization
7.10 Summary
8 Reliability of Mechanical Components and Systems
8.1 Introduction
Figure 8.1 Material behaviour in tensile stress.
8.2 Mechanical Stress, Strength and Fracture
Figure 8.2 Stress–strain for different materials (generalized).
8.3 Fatigue
Figure 8.3 S–N curve.
Figure 8.4 Random overload.
Example 8.1
Figure 8.5 S–N diagram for the part in Example 8.1.
Figure 8.6 Strength deterioration with cyclic stress.
Figure 8.7 Typical fatigue failure (schematic).
8.3.1 Design against Fatigue
8.3.2 Maintenance of Fatigue-Prone Components
8.4 Creep
8.5 Wear
8.5.1 Wear Mechanisms
8.5.2 Methods of Wear Reduction
8.5.3 Maintenance of Systems Subject to Wear
8.6 Corrosion
8.7 Vibration and Shock
8.8 Temperature Effects
Figure 8.8 Waterfall plot.
8.8.1 Humidity and Condensation
8.9 Materials
8.9.1 Metal Alloys
8.9.2 Plastics, Rubbers
8.9.3 Ceramics
8.9.4 Composites, Adhesives
8.10 Components
8.11 Processes
8.11.1 Fasteners
8.11.2 Adhesives
8.11.3 Welding and Soldering
8.11.4 Seals
Fracture mechanics
Vibration and shock
Materials and components
9 Electronic Systems Reliability
9.1 Introduction
9.2 Reliability of Electronic Components
9.2.1 Stress Effects Current Voltage
Figure 9.1 Parameter drift. Temperature
Figure 9.2 Temperature vs. reliability for electronic components. Power
9.3 Component Types and Failure Mechanisms
9.3.1 Integrated Circuits (ICs) Application-Specific ICs Microelectronics Packaging
Figure 9.3 Examples of electronic components. (a) Leadless chip capacitor (b) Quad flat pack IC package (QFP) (courtesy DfR Solutions) (c) Ball grid array (BGA) IC package.
Figure 9.4 Five stacked die 4 GB flash memory (pyramid stacking with wire bond interconnects). Hybrid /Microelectronic Packaging/Multichip Modules
Figure 9.5 Micro-hybrid. Microelectronic Component Attachment Microelectronic Device Failure Modes Microelectronic Device Specifications Microelectronic Device Screening
Figure 9.6 Typical failure density functions of electronic components when no component burn-in has been carried out.
Table 9.1 Microelectronic device screening requirementsa.
9.3.2 Other Electronic Components Discrete Semiconductors ‘Passive’ Components Capacitors Electro-Optical Components Cables and Connectors Insulation
9.3.3 Solder Tin-Lead Solder Lead-Free Solder
9.4 Summary of Device Failure Modes
Table 9.2 Device failure modes.
9.5 Circuit and System Aspects
9.5.1 Distortion and Jitter
9.5.2 Timing
9.5.3 Electromagnetic Interference and Compatibility
9.5.4 Intermittent Failures
9.5.5 Other Failure Causes
9.6 Reliability in Electronic System Design
9.6.1 Introduction
9.6.2 Transient Voltage Protection
Figure 9.7 Logic device protection. Diode D1 prevents the input voltage from rising above the power supply voltage. Capacitor C1 absorbs high frequency power supply transients.
Figure 9.8 Transistor protection. Resistor R1 limits the base current IB and capacitor C1 absorbs power supply high frequency transients.
9.6.3 Thermal Design
9.6.4 Stress Derating
Table 9.3 Device derating guidelines.
Figure 9.9 Temperature–power derating for transistors and diodes (typical).
9.6.5 Component Uprating
9.6.6 Electromagnetic Interference and Compatibility (EMI/EMC)
Figure 9.10 Digital circuit noise decoupling.
9.6.7 Redundancy
9.6.8 Design Simplification
9.6.9 Sneak Analysis
Figure 9.11 Sneak analysis basic patterns (hardware).
9.7 Parameter Variation and Tolerances
9.7.1 Introduction
Figure 9.12 Parameter distributions after selection.
9.7.2 Tolerance Design
9.7.3 Analysis Methods Worst Case Analysis The Transpose Circuit Simulation
Figure 9.13 Transpose circuit.
Figure 9.14 Monte Carlo analysis of filter circuit.
9.8 Design for Production, Test and Maintenance
Mechanical and thermal effects and design
Tolerance design and electronics testing
10 Software Reliability
10.1 Introduction
10.2 Software in Engineering Systems
Table 10.1 Comparison of Hardware and Software Reliability Characteristics.
10.3 Software Errors
10.3.1 Specification Errors
Figure 10.1 Voting redundant system.
10.3.2 Software System Design
10.3.3 Software Code Generation
10.4 Preventing Errors
10.4.1 Specification
10.5 Software Structure and Modularity
10.5.1 Structure
10.5.2 Modularity
Figure 10.2 Structured versus unstructured programming.
10.5.3 Requirements for Structured and Modular Programming
10.5.4 Software Re-Use
10.6 Programming Style
10.7 Fault Tolerance
10.8 Redundancy/Diversity
10.9 Languages
Figure 10.3 Fault tolerant algorithm.
10.10 Data Reliability
10.11 Software Checking
10.11.1 FMECA
10.11.2 Software Sneak Analysis
Figure 10.4 Software sneak patterns.
10.12 Software Testing
10.12.1 Managing Software Testing
10.13 Error Reporting
Figure 10.5 Software error reporting form.
10.14 Software Reliability Prediction and Measurement
10.14.1 Introduction
10.14.2 The Poisson Model (Time-Related)
10.14.3 The Musa Model
Example 10.1
10.14.4 The Jelinski–Moranda and Schick–Wolverton Models
10.14.5 Littlewood Models
Example 10.2
10.14.6 Point Process Analysis
10.15 Hardware/Software Interfaces
10.16 Conclusions
Figure 10.6 Software development for reliability.
11 Design of Experiments and Analysis of Variance
11.1 Introduction
11.2 Statistical Design of Experiments and Analysis of Variance
11.2.1 Analysis of Single Variables
Example 11.1
Table 11.1 Times to failure of 20 bearings.
Table 11.2 Values of for the data of Table 11.1.
Table 11.3 Values of for the data of Table 11.1.
Table 11.4 Values of WS for the data of Table 11.1.
Table 11.5 Sources of variance for the data in Table 11.1.
Table 11.6 Example 11.1 Minitab® solution.
Figure 11.1 Minitab® chart showing statistically significant difference between samples 1 and 4.
11.2.2 Analysis of Multiple Variables (Factorial Experiments)
Example 11.2
Table 11.7 Results of experiments on ‘O’ ring seals.
Table 11.8 The data of Table 11.7 after subtracting 100 from each datum.
Table 11.9 Analysis of variance table.
11.2.3 Non-Normally Distributed Variables
11.2.4 Two-Level Factorial Experiments
Example 11.3
Table 11.10 Results of a three-factor non-replicated experiment.
Table 11.11 Response table and interaction of effects A, B, C.
Table 11.12 Analysis of variance table.
11.2.5 Fractional Factorial Experiments
Figure 11.2 Main effects plots for Example 11.3 using Minitab®.
Table 11.13 The full design matrix for a 24 factorial experiment.
Table 11.14 Table 11.13 omitting rows where ABCD gives minus.
Table 11.15 Sixteenth fractional factorial layout for seven main effects.
11.3 Randomizing the Data
11.4 Engineering Interpretation of Results
Figure 11.3 Temperature–pressure interactions.
11.5 The Taguchi Method
Figure 11.4 Taguchi method (1).
Figure 11.5 Taguchi method (2).
Table 11.16 Results of Taguchi experiment on fuel system components (Example 11.4).
Example 11.4
Figure 11.6 Results of Taguchi experiment (Example 11.4).
11.6 Conclusions
12 Reliability Testing
12.1 Introduction
12.2 Planning Reliability Testing
12.2.1 Using Design Analysis Data
12.2.2 Considering Variability
12.2.3 Durability
12.3 Test Environments
Figure 12.1 Typical CERT environmental cycles: electronic equipment in a vehicle application.
Figure 12.2 CERT test facility
12.3.1 Vibration Testing
Figure 12.3 Road transport vibration levels.
12.3.2 Temperature Testing
12.3.3 Electromagnetic Compatibility (EMC) Testing
12.3.4 Other Environments
12.3.5 Customer Simulation Testing
12.4 Testing for Reliability and Durability: Accelerated Test
12.4.1 Test Development
Figure 12.4 Stress, strength and test failures (1).
Figure 12.5 Stress, strength and test failures (2).
Figure 12.6 Stress, strength and test failures (3): wearout failures.
12.4.2 Accelerated Test
Figure 12.7 Effect of accelerated test on the bathtub curve.
Figure 12.8 Stress ranges and types of failures.
12.4.3 Highly Accelerated Life Testing
12.4.4 Test Approach for Accelerated Test
12.4.5 HALT and Production Testing
Table 12.1 DoE/HALT Selection
12.4.6 DoE or HALT?
12.5 Test Planning
Figure 12.9 Example of a parallel test flow for an electronic device.
12.6 Failure Reporting, Analysis and Corrective Action Systems (FRACAS)
12.6.1 Failure Reporting
12.6.2 Corrective Action Effectiveness
13 Analysing Reliability Data
13.1 Introduction
13.2 Pareto Analysis
Figure 13.1 Pareto plot of failure data.
13.3 Accelerated Test Data Analysis
13.4 Acceleration Factor
Table 13.1 Acceleration factor relationship with reliability functions.
Example 13.1
13.5 Acceleration Models
13.5.1 Temperature and Humidity Acceleration Models Arrhenius Model
Table 13.2 Commonly used activation energy values for different failure mechanisms. Eyring Model Peck Temperature-Humidity Model Lawson Temperature-Humidity Model Tin-Lead Solder Lead-Free Solder
13.5.2 Voltage and Current Acceleration Models
13.5.3 Vibration Acceleration Models
Example 13.2
13.6 Field-Test Relationship
Example 13.3
13.7 Statistical Analysis of Accelerated Test Data
Table 13.3 Accelerated test results (Example 13.4).
Example 13.4
Figure 13.2 Weibull plot at three stress levels (Weibull++®)
Figure 13.3 Life vs. stress plot generated with ALTA® software
13.8 Reliability Analysis of Repairable Systems
13.8.1 Failure Rate of a Repairable System
Figure 13.4 Plotted data of Example 2.19.
13.8.2 Multisocket Systems
Figure 13.5 The failure pattern of a multisocket system.
Example 13.5 (Reprinted from Ascher and Feingold (1984) by courtesy of Marcel Dekker, Inc.)
13.9 CUSUM Charts
Figure 13.6 Bus engine failure data (a) First failures (191), (b) Second failures (105), (c) Third failures (101), (d) Fourth failures (96), (e) Fifth failures (94).
Table 13.4 Reliability test data Target = 95% (T).
Figure 13.7 (a) Run chart of data in Table 13.4. (b) CUSUM chart of data of Table 13.4.
13.10 Exploratory Data Analysis and Proportional Hazards Modelling
Figure 13.8 Time series chart: failure vs time (overhaul interval 1000 h).
13.11 Field and Warranty Data Analysis
13.11.1 Field and Warranty Data Considerations
Figure 13.9 Warranty claims root causes.
13.11.2 Warranty Data Formats Individual Claims Data Format Statistical Data Format
Table 13.5 Example of MIS (Month in Service) data (January – July 2011).
13.11.3 Warranty Data Processing
Table 13.6 Example of warranty data presented in the ‘Nevada’ (or the ‘Layer cake’) format.
Table 13.7 Cumulative percent failures for 6 months. Based on the data in Table 13.5.
Example 13.6
Figure 13.10 Weibull charts of the test results.
14 Reliability Demonstration and Growth
14.1 Introduction
14.2 Reliability Metrics
Example 14.1
14.3 Test to Success (Success Run Method)
14.3.1 Binomial Distribution Approach
Table 14.1 Required test sample sizes for reliability demonstration at 50 and 90% confidence.
14.3.2 Success Run Test with Undesirable Failures
14.4 Test to Failure Method
14.5 Extended Life Test
14.5.1 Parametric Binomial Method
Example 14.2
14.5.2 Limitations of the Parametric Binomial Model
14.6 Continuous Testing
Table 14.2 MTBF confidence limits.
Example 14.3
14.7 Degradation Analysis
Figure 14.1 Degradation analysis diagram.
14.8 Combining Results Using Bayesian Statistics
Example 14.4
14.9 Non-Parametric Methods
14.9.1 The C-Rank Method
Example 14.5
Figure 14.2 Solution to Example 14.2 using Weibull++® reliability demonstration calculator DRT
14.10 Reliability Demonstration Software
14.11 Practical Aspects of Reliability Demonstration
14.12 Standard Methods for Repairable Equipment
14.12.1 Probability Ratio Sequential Test (PRST) (US MIL-HDBK-781)
Figure 14.3 Typical probability ratio sequential test (PRST) plan.
14.12.2 Test Plans
Table 14.3 MIL-HDBK-781 PRST plans.
14.12.3 Statistical Basis for PRST Plans
Table 14.4 MIL-HDBK-781 fixed length test plans.
14.12.4 Operating Characteristic Curves and Expected Test Time Curves
14.12.5 Selection of Test Criteria
Figure 14.4 Operating characteristic (OC) curve. Test plan 1: α = 10%, β = 10% and d = 1.5.
Figure 14.5 Expected test time (ETT) curve. Test plan 1: α = 10%, β = 10% and d = 1.5.
14.12.6 Test Sample Size
14.12.7 Burn-In
14.12.8 Practical Problems of PRST
14.12.9 Reliability Demonstration for One-Shot Items
14.13 Reliability Growth Monitoring
14.13.1 The Duane Method
Figure 14.6 Duane reliability growth.
Example 14.6
Figure 14.7 Duane plot for Example 14.6.
Example 14.7
14.13.2 The M(t) Method
Figure 14.8 Duane plot for Example 14.7.
Figure 14.9 The M(t) analysis method
Example 14.8
Table 14.5 Service data.
Figure 14.10 M(t) graph of Table 14.5.
14.13.3 Reliability Growth Estimation by Failure Data Analysis
14.14 Making Reliability Grow
14.14.1 Test, Analyse and Fix
14.14.2 Reliability Growth in Service
15 Reliability in Manufacture
15.1 Introduction
15.2 Control of Production Variability
15.2.1 Process Capability
Figure 15.1 Process capability, Cp.
Figure 15.2 Process capability Cpk.
15.2.2 Process Control Charts
Figure 15.3 Process control charts.
15.3 Control of Human Variation
15.3.1 Inspection
15.3.2 Operator Control
15.4 Acceptance Sampling
15.4.1 Sampling by Attributes
15.4.2 Sampling by Variables
Table 15.1 Master table for normal inspection-single sampling (MIL-STD-105D, Table II-A).
Table 15.2 LTPD sampling plans.a Minimum size of sample to be tested to assure, with 90% confidence, that a lot having percentage defective equal to the specified LTPD will not be accepted (single sample).
Figure 15.4 Operating characteristic (OC) curves for single sampling plans (10% sample, acceptance number = 0).
15.4.3 General Comments on Sampling
15.5 Improving the Process
15.5.1 Simple Charts
Figure 15.5 Cause and effect diagram.
15.5.2 Control Charts
15.5.3 Multi-Vari Charts
Figure 15.6 Multi-vari chart.
15.5.4 Statistical Methods
15.5.5 ‘Zero Defects’
15.5.6 Quality Circles
15.6 Quality Control in Electronics Production
15.6.1 Test Methods Vision Systems In-Circuit Testers (ICT), Manufacturing Defects Analysers (MDA) Functional Testers (FT)
Figure 15.7 Electronic equipment test strategy.
15.6.2 Reliability of Connections
15.7 Stress Screening
15.7.1 Highly Accelerated Stress Screening
Figure 15.8 HASS.
15.8 Production Failure Reporting Analysis and Corrective Action System (FRACAS)
15.9 Conclusions
16 Maintainability, Maintenance and Availability
16.1 Introduction
16.2 Availability Measures
16.2.1 Inherent Availability
16.2.2 Achieved Availability
16.2.3 Operational Availability
Example 16.1
16.3 Maintenance Time Distributions
Figure 16.1 The lognormal distribution of maintenance times.
16.4 Preventive Maintenance Strategy
Figure 16.2 Theoretical reliability and scheduled replacement relationships.
Figure 16.3 RCM logic.
Example 16.2
16.4.1 Practical Implications
16.5 FMECA and FTA in Maintenance Planning
16.6 Maintenance Schedules
16.7 Technology Aspects
16.7.1 Mechanical
16.7.2 Electronic and Electrical
16.7.3 ‘No Fault Found’
16.7.4 Software
16.7.5 Built-in Test (BIT)
16.8 Calibration
16.9 Maintainability Prediction
16.10 Maintainability Demonstration
16.11 Design for Maintainability
16.12 Integrated Logistic Support
17 Reliability Management
17.1 Corporate Policy for Reliability
17.2 Integrated Reliability Programmes
Figure 17.1 Reliability Programme Flow (design/development).
Figure 17.2 Reliability Programme Flow (production, in-service).
17.3 Reliability and Costs
17.3.1 Costs of Reliability
17.3.2 Costs of Unreliability
Example 17.1
Figure 17.3 Reliability cost progression (Example 17.1).
17.4 Safety and Product Liability
17.5 Standards for Reliability, Quality and Safety
17.5.1 ISO/IEC60300 (Dependability)
17.5.2 ISO9000 (Quality Systems)
17.5.3 IEC61508 (Functional Safety of Electrical/Electronic/Programmable Electronic Safety-Related Systems)
17.6 Specifying Reliability
17.6.1 Definition of Failure
17.6.2 Environmental Specifications
17.6.3 Stating the Reliability Requirement
17.7 Contracting for Reliability Achievement
Figure 17.4 Reliability incentive structure.
17.7.1 Warranty Improvement Contracts
17.7.2 Total Service Contracts
17.8 Managing Lower-Level Suppliers
17.9 The Reliability Manual
Table 17.1 Reliability manual: responsibilities.
17.10 The Project Reliability Plan
17.10.1 Specification Tailoring
17.11 Use of External Services
17.12 Customer Management of Reliability
Example 17.2
17.13 Selecting and Training for Reliability
17.14 Organization for Reliability
Figure 17.5 QA based reliability organization.
17.14.1 Quality Assurance Based Organization
17.14.2 Engineering Based Organization
17.14.3 Comparison of Types of Organization
Figure 17.6 Engineering based reliability organization.
17.15 Reliability Capability and Maturity of an Organization
17.15.1 Reliability Capability
17.15.2 Reliability Maturity
17.16 Managing Production Quality
17.16.1 Setting Production Quality Criteria
17.16.2 Monitoring Production Quality Performance and Costs
17.16.3 Quality Training
17.16.4 Specialist Facilities and Services
17.16.5 Quality Audit and Registration
17.17 Quality Management Approaches
17.17.1 Quality Systems
17.17.2 ‘Six Sigma’
17.17.3 Quality Circles
17.17.4 Quality Awards
17.17.5 Total Quality Management
17.18 Choosing the Methods: Strategy and Tactics
17.19 Conclusions
Back Matter
Appendix 1 The Standard Cumulative Normal Distribution Function
Appendix 2 χ2(α, v) Distribution Values
Appendix 3 Kolmogorov–Smirnov Tables
Appendix 4 Rank Tables (5%, 95%)
Appendix 5 Failure Reporting, Analysis and Corrective Action System (FRACAS)
Appendix 6 Reliability, Maintainability (and Safety) Plan Example
1.1 Introduction
1.2 Reliability, availability, maintainability and safety (RAMS) requirements
1.3 RAMS tasks
2.1 Reliability modelling
2.2 Reliability prediction and apportionment
2.3 Failure modes, effects and criticality analysis
2.4 Fault tree analysis
2.5 Reliability testing
2.6 Failure reporting and RAMS monitoring
2.7 Production reliability assurance
2.8 Maintainability analysis and demonstration
2.9 In-service RAMS monitoring (ISRM)
3.1 Preliminary hazard analysis
3.2 System and sub-system hazard analysis
3.3 Hazard tracking (Hazard log)
4.1 Responsibilities
4.2 RAMS reviews
Appendix 7 Matrix Algebra Revision

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