Muutke küpsiste eelistusi

E-raamat: Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications

4.00/5 (1 hinnangut Goodreads-ist)
, ,
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 184,63 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and ApplicationsSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail.The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear

degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.

Part I Introduction, Basic Concepts and Preliminaries.- Overview.- Advances in Data-Driven Remaining Useful Life Prognosis.- Model Determination for Lifetime Prognosis.- Part II Remaining Useful Life Prognosis for Linear Stochastic Degrading Systems.- Adaptive Remaining Useful Life Prediction Method.- Exact and Closed-Form Solution to Remaining Useful Life Prediction.- Remaining Useful Life Prediction With Three-Level Variability.- Part III Remaining Useful Life Prognosis for Nonlinear Stochastic Degrading Systems.- Nonlinear Degradation Modeling and Remaining Useful Life Prediction Method.- Hidden and Nonlinear Degradation Modeling and Online Remaining Useful Life Prediction Method.- Remaining Useful Life Prediction Method With State Switches.- Remaining Useful Life Prediction for Degrading Systems with Imperfect Maintenance.- Part IV Applications of Prognostics in Decision Making.- Variable Cost-based Maintenance Model from Prognostic Information.- Forecasting Spare Parts Demand

under Prognostic Information.- Variable Cost-Based Maintenance and Inventory Model from Prognostic Information.- Prognostic-information-Based Order-Replacement Policy for Degrading Systems.
Part I Introduction, Degradation Data Acquisition and Evaluation
1 Advances in Data-Driven RUL Prognosis Techniques
3(20)
1.1 Introduction
3(2)
1.2 Methods Considering Unit-to-Unit Variability
5(3)
1.2.1 Random Coefficients Regression Models
6(1)
1.2.2 Stochastic Process Models with Random Coefficients
6(2)
1.3 Methods Considering Impact of Heterogeneity in Working Environment
8(5)
1.3.1 Methods Based on Stochastic Filtering
8(1)
1.3.2 Multi-stage Degradation Models
9(1)
1.3.3 Covariate Hazards Model
10(1)
1.3.4 Degradation Models Involving Random Shocks
11(2)
1.4 Methods Considering the Impact of Tasks and Workloads
13(2)
1.4.1 Degradation Modeling for Systems with Dynamic Workloads
13(1)
1.4.2 Degradation Modeling for System with Maintenances
14(1)
1.5 Future Research Directions
15(8)
References
17(6)
2 Planning Repeated Degradation Testing for Degrading Products
23(16)
2.1 Introduction
23(2)
2.2 Degradation Modeling with Three-Source Variability
25(2)
2.3 Parameter Estimation and Information Matrix
27(1)
2.4 Estimating the Degradation Distribution and Lifetime Distribution
28(4)
2.4.1 The Quantiles of Degradation Distribution and Its Variance
28(1)
2.4.2 The Lifetime Distribution
29(3)
2.5 Degradation Test Planning
32(1)
2.6 An Illustrative Example
33(6)
References
36(3)
3 Specifying Measurement Errors for Required Lifetime Estimation Performance
39(34)
3.1 Introduction
39(3)
3.2 Properties of the WPDM
42(1)
3.3 Properties of the WPDM with the ME
43(2)
3.4 Permissible ME Parameters for Lifetime Estimation
45(5)
3.4.1 Performance Measures to Quantify the Difference in Lifetime Estimation with Versus Without the ME
45(1)
3.4.2 Permissible ME Parameters Using the Relative Increase Ratio of the CV
46(3)
3.4.3 Permissible ME Parameters Using the Relative Increase Ratio of the Variance
49(1)
3.5 Effect of Lifetime Estimation with or Without ME on an Age-Based Replacement Decision
50(2)
3.6 Experimental Studies
52(21)
3.6.1 A Numerical Illustration
52(3)
3.6.2 The Case Study
55(7)
Appendix
62(5)
References
67(6)
Part II Prognostic Techniques for Linear Degrading Systems
4 An Adaptive Remaining Useful Life Estimation Approach with a Recursive Filter
73(30)
4.1 Introduction
73(3)
4.2 Wiener-Process-Based Degradation Modeling and RUL Estimation
76(8)
4.2.1 An Outline of Wiener-Process-Based Degradation Model for Lifetime Analysis
76(3)
4.2.2 Wiener-Process-Based Degradation Modeling
79(2)
4.2.3 Real-Time Updating of the RUL Distribution
81(3)
4.3 Parameter Estimation
84(8)
4.3.1 EM Algorithm
84(2)
4.3.2 The Implementation of EM Algorithm for the Proposed Model
86(4)
4.3.3 Convergence Analysis of Adaptive Model Parameter Estimation Algorithm
90(2)
4.4 A Practical Case Study
92(11)
4.4.1 Problem Description
92(2)
4.4.2 The Implementation of Our Model for RUL Estimation of the INS
94(2)
4.4.3 Comparative Studies
96(4)
References
100(3)
5 An Exact and Closed-Form Solution to Degradation Path-Dependent RUL Estimation
103(40)
5.1 Introduction
103(4)
5.2 A Degradation Path-Dependent Approach for Adaptive RUL Estimation
107(3)
5.2.1 A General Description of Stochastic Process Based Degradation Models
107(1)
5.2.2 A Degradation Path-Dependent Approach for Adaptive RUL Estimation via Real-Time CM Data
108(2)
5.3 Linear Model'
110(13)
5.4 Exponential Model
123(7)
5.5 Experimental Studies
130(13)
5.5.1 Numerical Example
131(5)
5.5.2 A Practical Case Study of the Developed Approach in Condition-Based Replacement
136(5)
References
141(2)
6 Estimating RUL with Three-Source Variability in Degradation Modeling
143(40)
6.1 Introduction
143(4)
6.1.1 Motivation
143(2)
6.1.2 Related Works
145(1)
6.1.3 Main Works of This
Chapter
146(1)
6.2 Description of Degradation Modeling with Three-Source Variability for RUL Estimation
147(2)
6.3 RUL Estimation with Three-Source Variability
149(18)
6.3.1 RUL Estimation with Temporal Variability and Unit-to-Unit Variability
149(5)
6.3.2 RUL Estimation with Temporal Variability and Uncertain Measurements
154(4)
6.3.3 RUL Estimation with Three-Source Variability
158(9)
6.4 Parameter Estimation
167(3)
6.5 Experimental Studies
170(13)
6.5.1 Problem Description
171(3)
6.5.2 Comparisons for Model Fitting
174(1)
6.5.3 Comparisons for the Estimated RUL
175(3)
References
178(5)
Part III Prognostic Techniques for Nonlinear Degrading Systems
7 RUL Estimation Based on a Nonlinear Diffusion Degradation Process
183(34)
7.1 Introduction
183(3)
7.2 Literature Review
186(1)
7.3 Motivating Examples and RUL Modeling Principle
187(4)
7.4 Lifetime Distribution and Parameter Estimation of the Proposed Degradation Model
191(11)
7.4.1 Derivation of the Lifetime Distribution
191(7)
7.4.2 Lifetime Distribution Under Random Effects
198(2)
7.4.3 The Distribution of the RUL Estimation
200(2)
7.5 Parameters Estimation
202(3)
7.6 Examples of the Applications of the Models
205(12)
7.6.1 Laser Data
206(2)
7.6.2 Drift Degradation Data of INS
208(3)
7.6.3 Fatigue Crack Data of 2017-T4
211(2)
References
213(4)
8 Prognostics for Age- and State-Dependent Nonlinear Degrading Systems
217(30)
8.1 Introduction
217(2)
8.2 Problem Formulation
219(2)
8.3 RUL Estimation by Degradation Modeling
221(5)
8.4 Model Parameter Estimation Framework
226(2)
8.5 An Illustrative Example
228(4)
8.5.1 Degradation Model and Lifetime Estimation
228(2)
8.5.2 Parameters Estimation
230(1)
8.5.3 Verifying the Accuracy of the Proposed Method
231(1)
8.6 Case Study
232(15)
References
243(4)
9 Adaptive Prognostic Approach via Nonlinear Degradation Modeling
247(26)
9.1 Introduction
247(3)
9.2 Nonlinear Model Description and RUL Estimation
250(6)
9.2.1 Modeling Description
250(1)
9.2.2 Derivation of the RUL Distribution
251(2)
9.2.3 Adaptive RUL Estimation
253(3)
9.3 Adaptive Parameter Estimation
256(4)
9.4 An Illustrative Example
260(1)
9.5 Numerical Example and Case Study
261(12)
9.5.1 Numerical Example
261(4)
9.5.2 Lithium-Ion Battery Life Prognosis
265(3)
References
268(5)
10 Prognostics for Hidden and Age-Dependent Nonlinear Degrading Systems
273(40)
10.1 Introduction
273(4)
10.1.1 Motivation
273(1)
10.1.2 Related Works
274(2)
10.1.3 Main Works of This
Chapter
276(1)
10.2 Problem Formulation and RUL Estimation
277(10)
10.2.1 Problem Formulation
277(2)
10.2.2 RUL Estimation
279(5)
10.2.3 Comparative Discussions
284(3)
10.3 Parameter Estimation
287(4)
10.4 Illustrative Examples
291(6)
10.4.1 The Derivation of the RUL for Three Cases
291(2)
10.4.2 The Derivation of Parameter Estimation Algorithm for Three Cases
293(4)
10.5 Simulation Study
297(8)
10.6 Case Study
305(8)
10.6.1 The Data and State-Space-Based Degradation Model
305(2)
10.6.2 Results and Discussions
307(2)
References
309(4)
11 Prognostics for Nonlinear Degrading Systems with Three-Source Variability
313(24)
11.1 Introduction
313(2)
11.2 Nonlinear Prognostic Model Description
315(2)
11.3 RUL Estimate Method with Three-Source Variability
317(11)
11.3.1 RUL Estimate Only with the Temporal Variability
317(1)
11.3.2 RUL Estimate with the Temporal Variability and the Unit-to-Unit Variability
318(3)
11.3.3 RUL Estimate with the Temporal Variability and the Measurement Variability
321(3)
11.3.4 RUL Estimate with Three-Source Variability
324(4)
11.3.5 Parameter Estimation
328(1)
11.4 Experimental Studies
328(9)
11.4.1 Simulation Study
329(3)
11.4.2 Case Study
332(3)
References
335(2)
12 RSL Prediction Approach for Systems with Operation State Switches
337(26)
12.1 Introduction
337(2)
12.2 Literature Review
339(1)
12.3 Problem Description for RSL Estimation
340(2)
12.4 Model Formulation for Transitions Between the Operating State and Storage State
342(4)
12.4.1 Randomly Varying System Operation Process
342(2)
12.4.2 Bayesian Estimation for Parameters in the System's Operation Process
344(2)
12.5 Model Formulation of the System Degradation Process to Predict the RSL
346(7)
12.5.1 Predicting the RSL Conditional on the Model Parameters and Fixed System Operation Process
346(3)
12.5.2 Bayesian Estimation for Parameters in the Degradation Process
349(1)
12.5.3 RSL Prediction Considering the Future Transitions and Updated Parameters
350(3)
12.6 Case Study
353(10)
12.6.1 Background and Data Description
353(4)
12.6.2 Results and Discussions
357(2)
References
359(4)
Part IV Applications of Prognostic Information
13 Reliability Estimation Approach for PMS
363(30)
13.1 Introduction
363(3)
13.2 Assumptions and Problem Description
366(2)
13.2.1 Problem Description
366(1)
13.2.2 Assumptions
367(1)
13.3 Mission Process to Estimate the Mission Time
368(6)
13.4 System Degradation Process to Estimate the Lifetime
374(8)
13.4.1 Model Description
374(2)
13.4.2 Bayesian Updating of Model Parameters
376(1)
13.4.3 Estimating the RUL of PMS
377(5)
13.5 Reliability Estimation for PMS
382(1)
13.6 Experimental Studies
383(10)
13.6.1 Numerical Simulations
383(5)
13.6.2 Case Study
388(2)
References
390(3)
14 A Real-Time Variable Cost-Based Maintenance Model
393(12)
14.1 Introduction
393(2)
14.2 Degradation Modeling for Prognostics
395(4)
14.2.1 Degradation Modeling
395(3)
14.2.2 RUL Estimation
398(1)
14.3 Replacement Decision Modeling
399(2)
14.4 A Case Study
401(4)
References
403(2)
15 An Adaptive Spare Parts Demand Forecasting Method Based on Degradation Modeling
405(14)
15.1 Introduction
405(2)
15.2 Degradation Modeling Description
407(1)
15.3 Adaptive Lifetime Estimation
408(2)
15.4 Adaptively Forecasting Spare Parts Demand
410(2)
15.5 Adaptive Parameter Estimation
412(1)
15.6 Case Study
413(6)
References
416(3)
16 Variable Cost-Based Maintenance and Inventory Model
419(11)
16.1 Introduction
419(1)
16.2 Degradation Modeling for Prognostics
420(2)
16.2.1 Degradation Modeling
421(1)
16.2.2 RUL Estimation
421(1)
16.3 Parameter Estimation
422(2)
16.4 Replacement and Inventory Decision Modeling
424(3)
16.5 Case Study
427(3)
References 430
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97836625403052e.html
Märksõnad: