Life Data Analysis Reference

Life Data Analysis Reference

438 Pages · 2015 · 14.85 MB · English

Where the Licensed Rights include Sui Generis Database Rights that apply to incidental, consequential, punitive, exemplary, or other losses, costs, . Risk Analysis and Probabilistic Design with Monte Carlo Simulation . Reliability engineering provides the theoretical and practical tools whereby th

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Contents Articles Chapter 1 1 Introduction to Life Data Analysis 1 Chapter 2 10 Basic Statistical Background 10 Chapter 3 16 Life Distributions 16 Chapter 4 23 Parameter Estimation 23 Chapter 5 41 Life Data Classification 41 Chapter 6 48 Confidence Bounds 48 Chapter 7 71 The Exponential Distribution 71 Chapter 8 103 The Weibull Distribution 103 Chapter 9 155 The Normal Distribution 155 Chapter 10 187 The Lognormal Distribution 187 Chapter 11 214 The Mixed Weibull Distribution 214 Chapter 12 225 The Generalized Gamma Distribution 225 Chapter 13 231 The Gamma Distribution 231 Chapter 14 237 The Logistic Distribution 237 Chapter 15 246 The Loglogistic Distribution 246 Chapter 16 251 The Gumbel/SEV Distribution 251 Chapter 17 257 NonParametric Life Data Analysis 257 Chapter 18 264 Competing Failure Modes Analysis 264 Chapter 19 274 Warranty Data Analysis 274 Chapter 20 305 Recurrent Event Data Analysis 305 Chapter 21 319 Degradation Data Analysis 319 Chapter 22 329 Reliability Test Design 329 Chapter 23 351 StressStrength Analysis 351 Comparing Life Data Sets 361 Risk Analysis and Probabilistic Design with Monte Carlo Simulation 365 Weibull++ SimuMatic 372 Target Reliability Tool 375 Event Log Data Analysis 383 Appendices 395 Least Squares/Rank Regression Equations 395 Appendix: Maximum Likelihood Estimation Example 400 Appendix: Special Analysis Methods 404 Appendix: LogLikelihood Equations 410 Appendix: Life Data Analysis References 425 References Article Sources and Contributors 427 Image Sources, Licenses and Contributors 428 Article Licenses License 433 1 Chapter 1 Introduction to Life Data Analysis Reliability Life Data Analysis refers to the study and modeling of observed product lives Life data can be lifetimes of products in the marketplace, such as the time the product operated successfully or the time the product operated before it failed These lifetimes can be measured in hours, miles, cyclestofailure, stress cycles or any other metric with which the life or exposure of a product can be measured All such data of product lifetimes can be encompassed in the term life data or, more specifically, product life data The subsequent analysis and prediction are described as life data analysis For the purpose of this reference, we will limit our examples and discussions to lifetimes of inanimate objects, such as equipment, components and systems as they apply to reliability engineering, however the same concepts can be applied in other areas An Overview of Basic Concepts When performing life data analysis \(also commonly referred to as Weibull analysis \), the practitioner attempts to make predictions about the life of all products in the population by fitting a statistical distribution \(model\) to life data from a representative sample of units The parameterized distribution for the data set can then be used to estimate important life characteristics of the product such as reliability or probability of failure at a specific time, the mean life and the failure rate Life data analysis requires the practitioner to: 1 1 Gather life data for the product 2 2 Select a lifetime distribution that will fit the data and model the life of the product 3 3 Estimate the parameters that will fit the distribution to the data 4 4 Generate plots and results that estimate the life characteristics of the product, such as the reliability

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