Atnaujinkite slapukų nuostatas

El. knyga: Prognostics and Health Management of Engineering Systems: An Introduction

4.00/5 (1 ratings by Goodreads)
, ,
  • Formatas: EPUB+DRM
  • Išleidimo metai: 24-Oct-2016
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319447421
Kitos knygos pagal šią temą:
Prognostics and Health Management of Engineering Systems: An IntroductionDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Išleidimo metai: 24-Oct-2016
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319447421
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

This book introduces the methods for predicting the future behavior of a system"s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application. Among the many topics discussed in-depth are: - Prognostics tutorials using least-squares - Bayesian inference and parameter estimation - Physics-based prognostics alg

orithms including nonlinear least squares, Bayesian method, and particle filter - Data-driven prognostics algorithms including Gaussian process regression and neural network - Comparison of different prognostics algorithms The authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in the above fields.

Introduction.- Tutorials for Prognostics.- Bayesian Statistics for Prognostics.- Physics-Based Prognostics.- Data-Driven Prognostics.- Study on Attributes of Prognostic Methods.- Applications of Prognostics.
1 Introduction
1(24)
1.1 Prognostics and Health Management
1(4)
1.2 Historical Background
5(3)
1.3 PHM Applications
8(2)
1.4 Review of Prognostics Algorithms
10(4)
1.5 Benefits and Challenges for Prognostics
14(11)
1.5.1 Benefits in Life-Cycle Cost
14(1)
1.5.2 Benefits in System Design and Development
15(1)
1.5.3 Benefits in Production
16(1)
1.5.4 Benefits in System Operation
16(1)
1.5.5 Benefits in Logistics Support and Maintenance
17(1)
1.5.6 Challenges in Prognostics
18(3)
References
21(4)
2 Tutorials for Prognostics
25(48)
2.1 Introduction
25(3)
2.2 Prediction of Degradation Behavior
28(16)
2.2.1 Least Squares Method
28(3)
2.2.2 When a Degradation Model Is Available (Physics-Based Approaches)
31(7)
2.2.3 When a Degradation Model Is NOT Available (Data-Driven Approaches)
38(6)
2.3 RUL Prediction
44(9)
2.3.1 RUL
44(5)
2.3.2 Prognostics Metrics
49(4)
2.4 Uncertainty
53(15)
2.5 Issues in Practical Prognostics
68(1)
2.6 Exercises
69(4)
References
70(3)
3 Bayesian Statistics for Prognostics
73(54)
3.1 Introduction to Bayesian Theory
73(3)
3.2 Aleatory Uncertainty versus Epistemic Uncertainty
76(10)
3.2.1 Aleatory Uncertainty
76(2)
3.2.2 Epistemic Uncertainty
78(2)
3.2.3 Sampling Uncertainty in Coupon Tests
80(6)
3.3 Conditional Probability and Total Probability
86(7)
3.3.1 Conditional Probability
86(6)
3.3.2 Total Probability
92(1)
3.4 Bayes' Theorem
93(11)
3.4.1 Bayes' Theorem in Probability Form
93(2)
3.4.2 Bayes' Theorem in Probability Density Form
95(4)
3.4.3 Bayes' Theorem with Multiple Data
99(3)
3.4.4 Bayes' Theorem for Parameter Estimation
102(2)
3.5 Bayesian Updating
104(6)
3.5.1 Recursive Bayesian Update
104(4)
3.5.2 Overall Bayesian Update
108(2)
3.6 Bayesian Parameter Estimation
110(4)
3.7 Generating Samples from Posterior Distribution
114(8)
3.7.1 Inverse CDF Method
114(2)
3.7.2 Grid Approximation Method: One Parameter
116(3)
3.7.3 Grid Approximation: Two Parameters
119(3)
3.8 Exercises
122(5)
References
124(3)
4 Physics-Based Prognostics
127(52)
4.1 Introduction to Physics-Based Prognostics
127(4)
4.1.1 Demonstration Problem: Battery Degradation
130(1)
4.2 Nonlinear Least Squares (NLS)
131(9)
4.2.1 MATLAB Implementation of Battery Degradation Prognostics Using Nonlinear Least Squares
133(7)
4.3 Bayesian Method (BM)
140(12)
4.3.1 Markov Chain Monte Carlo (MCMC) Sampling Method
140(7)
4.3.2 MATLAB Implementation of Bayesian Method for Battery Prognostics
147(5)
4.4 Particle Filter (PF)
152(13)
4.4.1 SIR Process
154(6)
4.4.2 MATLAB Implementation of Battery Prognostics
160(5)
4.5 Practical Application of Physics-Based Prognostics
165(7)
4.5.1 Problem Definition
165(2)
4.5.2 Modifying the Codes for the Crack Growth Example
167(3)
4.5.3 Results
170(2)
4.6 Issues in Physics-Based Prognostics
172(4)
4.6.1 Model Adequacy
173(1)
4.6.2 Parameter Estimation
174(1)
4.6.3 Quality of Degradation Data
175(1)
4.7 Exercise
176(3)
References
177(2)
5 Data-Driven Prognostics
179(64)
5.1 Introduction to Data-Driven Prognostics
179(2)
5.2 Gaussian Process (GP) Regression
181(26)
5.2.1 Surrogate Model and Extrapolation
181(2)
5.2.2 Gaussian Process Simulation
183(4)
5.2.3 GP Simulation
187(14)
5.2.4 MATLAB Implementation of Battery Prognostics Using Gaussian Process
201(6)
5.3 Neural Network (NN)
207(19)
5.3.1 Feedforward Neural Network Model
208(13)
5.3.2 MATLAB Implementation of Battery Prognostics Using Neural Network
221(5)
5.4 Practical Use of Data-Driven Approaches
226(6)
5.4.1 Problem Definition
226(2)
5.4.2 MATLAB Codes for the Crack Growth Example
228(2)
5.4.3 Results
230(2)
5.5 Issues in Data-Driven Prognostics
232(4)
5.5.1 Model-Form Adequacy
232(1)
5.5.2 Optimal Parameters Estimation
233(2)
5.5.3 Quality of Degradation Data
235(1)
5.6 Exercise
236(7)
References
238(5)
6 Study on Attributes of Prognostics Methods
243(38)
6.1 Introduction
243(2)
6.2 Problem Definition
245(7)
6.2.1 Paris Model for Fatigue Crack Growth
245(2)
6.2.2 Huang's Model for Fatigue Crack Growth
247(3)
6.2.3 Health Monitoring Data and Loading Conditions
250(2)
6.3 Physics-Based Prognostics
252(17)
6.3.1 Correlation in Model Parameters
253(10)
6.3.2 Comparison of NLS, BM, and PF
263(6)
6.4 Data-Driven Prognostics
269(5)
6.4.1 Comparison Between GP and NN
270(4)
6.5 Comparison Between Physics-Based and Data-Driven Prognostics
274(1)
6.6 Results Summary
275(1)
6.7 Exercise
276(5)
References
279(2)
7 Applications of Prognostics
281(61)
7.1 Introduction
281(1)
7.2 In Situ Monitoring and Prediction of Joint Wear
282(16)
7.2.1 Motivation and Background
282(1)
7.2.2 Wear Model and Wear Coefficient
283(2)
7.2.3 In Situ Measurement of Joint Wear for a Slider-Crank Mechanism
285(3)
7.2.4 Bayesian Inference for Predicting Progressive Joint Wear
288(4)
7.2.5 Identification of Wear Coefficient and Prediction of Wear Volume
292(4)
7.2.6 Discussion and Conclusions
296(2)
7.3 Identification of Correlated Damage Parameters Under Noise and Bias Using Bayesian Inference
298(11)
7.3.1 Motivation and Background
298(1)
7.3.2 Damage Growth and Measurement Uncertainty Models
299(2)
7.3.3 Bayesian Inference for Characterization of Damage Properties
301(8)
7.3.4 Conclusions
309(1)
7.4 Usage of Accelerated Test Data for Predicting Remaining Useful Life at Field Operating Conditions
309(12)
7.4.1 Motivation and Background
310(1)
7.4.2 Problem Definition
311(1)
7.4.3 Utilizing Accelerated Life Test Data
312(9)
7.4.4 Conclusions
321(1)
7.5 Bearing Prognostics Method Based on Entropy Decrease at Specific Frequencies
321(18)
7.5.1 Motivation and Background
321(3)
7.5.2 Degradation Feature Extraction
324(7)
7.5.3 Prognostics
331(5)
7.5.4 Discussions on Generality of the Proposed Method
336(2)
7.5.5 Conclusions and Future Works
338(1)
7.6 Other Applications
339(3)
References 342(3)
Index 345
Dr. Nam-Ho Kim is Professor of Mechanical and Aerospace Engineering at the University of Florida. His research areas is structural design optimization, design sensitivity analysis, design under uncertainty, structural health monitoring, nonlinear structural mechanics, and structural-acoustics. He has published three books and more than hundred refereed journal and conference papers in the above areas. Dr. Dawn An received a Bachelor and Master of mechanical engineering from Korea Aerospace University in 2008 and 2010, respectively. She started a joint Ph.D. at Korea Aerospace University and the University of Florida in 2011, and received her Ph.D. in 2015 as a jointly conferred degree. She is now a postdoctoral associate at the University of Florida. Her current research is focused on enhancing prognostics methods for real damage data having limitation in terms of insufficient number of data and large noise in data without physical model. Joo Ho Choi is Professor in the School of Aerospace and Mechanical Engineering, Korea Aerospace University.
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97833194474216e.html
Raktažodžiai: