Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and

169 Pages·2016·18.39 MB·English

Modeling of Machine Life Using
Accelerated Prognostics and Health Management (APHM)
and Enhanced Deep Learning Methodology

A dissertation submitted to the Graduate School
Of the University of Cincinnati

in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
In the Department of Mechanical and Materials Engineering
Of the College of Engineering and Applied Science

by

Wenjing Jin

August 2016

Committee Chair: Professor Jay Lee
Committee Member: Professor Teik Lim
Committee Member: Professor David Thompson
Committee Member: Dr. Linxia Liao
ABSTRACT

Machine health monitoring has advanced significantly for improving machine uptime
and efficiency by providing proper fault detection and remaining useful life (RUL)
prediction information to machine users. Despite these advancements, conventional
condition monitoring (CM) techniques face several challenges in machine prognostics,
including the ineffective RUL prediction modeling for machine under dynamic working
regimes, and the lack of complete lifecycle data for modeling and validation, among
others. To address these issues, this research introduces Accelerated Degradation Tests
(ADT) with a deep learning technique, which is a novel method to improve machine life
prediction accuracy under different working regimes for Prognostics and Health
Management applications.
This dissertation work highlights the mathematical framework of deep learning based
machine life modeling under an ADT environment, including Constant Stress Accelerated
Degradation Testing (CSADT) and Step-Stress ADT (SSADT) conditions. Since most CM
features show no trend or indication of failure until a machine is approaching the end of
its life, current RUL prediction techniques are not applicable in that they are only effective
when incipient degradation is detected. This dissertation work applies feature
enhancement to condition-based features using the enhanced Restricted Boltzmann
Machine (RBM) method with a prognosability regularization term; afterwards, a similarity-
based method is applied to predict machine life with the enhanced RBM features. In
addition, this research has added varying stress conditions during experiments to
replicate dynamic operation regimes. The stress variable, a type of regime variables, is
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input into Mixed-Variate RBM (MV-RBM) model. Therefore, a Regime Matrix based RBM
(RM-RBM) is proposed to improve the feature prognosability and reduce the impact that
the working stresses have on the features. Then the RBM features can be fused into a
single health value which reflects the machine degradation. Finally, the developed
machine “life-stress-degradation” model can effectively estimate the machine life under
any given stress.
The feasibility study is demonstrated through three groups of rotary machinery
components run-to-failure tests datasets. The first two case studies focus on CSADT from
two bearing run-to-failure test-beds. The first in-house bearing test demonstrates the
effectiveness of applying an enhanced RBM with a prognosability regularization term to
improve predictability of both the features and health value. The second bearing test
dataset utilizes a similarity-based method to benchmark the RUL prediction results of the
RBM features with other feature extraction methods. The third case study focuses on the
issue of dynamic operating regimes; it is validated through a step-stress accelerated
degradation test on a ball screw system. By integrating both the RM-RBM model and
SSADT model in the PHM analysis framework, an innovative condition-based life-stress
model for the linear motion system will be demonstrated.
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ACKNOWLEDGMENTS

I would like to express great appreciation to Professor Jay Lee for his guidance and
support through my Ph.D. dissertation at the University of Cincinnati. I am also grateful
to Professor Teik Lim, Professor David Thompson, and Dr. Linxia Liao for serving on my
doctoral dissertation committee.
I would like to express my gratitude to Mr. Patrick Brown for all the help he provided
with proposals, budgets, administrative aspects, and marketing material that made
working in the IMS Center an enjoyable experience. In addition, I appreciate the
organizational and administrative skills of Mr. Michael Lyons, who helped to organize
customer meetings and several conferences, and allowed myself and other researchers
to not worry about these administrative aspects. I am also thankful to Dr. Yan Chen, Dr.
David Siegel, Dr. Edzel Lapira, Dr. Mohamed AbuAli, and Dr. Masoud Ghaffari, for the
guidance they provided me during their time as post-doctoral researchers at the IMS
Center. I also want to thank to my teammates great efforts and hard work on the linear
motion system experiments, Mr. Matthew Buzza, Mr. Jianshe Feng, Mr. Pin Li, and Ms.
Fally Titikpina. I also appreciate all the support and collaborative work I did with my
colleagues during my Ph.D.’s studies, including Mr. Hossein Davari Ardakani, Mr. Chao
Jin, Mr. Zhe Shi, Mr. Zongchang Liu, Mr. Behrad Bagheri, Mr. Yuan Di, Mr. Shanhu Yang,
Mr. Xiaodong Jia, Ms. Laura Henkel, Mr. Shaojie Wang, Mr. Wenyu Zhao, Mr.
Muhammad Rezvani, Ms. Ann Kao, Ms. Christina Lucas, Ms. Paige Weaver, Ms. Ellen
Gamel, Mr. Arian Houshmand, Ms. Maria Holgado, Mr. Fangcheng Yuan, Dr. Jian Chen,
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Ms. Kaitlin Burnam, Mr. Brian Phillips, Mr. Nick Manning, Mr. Chuan Jiang, Mr. Eric Huang,
Ms. Xiaorui Tong, Mr. Tyler Skirtich, Mr. Feibai Zhu, Mr. Su Xu, Mr. Ziyi Wang, Mr. Shuo
Chang, Dr. Lei Zhang, Dr. Yixiang Huang, Dr. Chein Chung Sun, Dr. Weihua Li, Ms.
Meihui Wang, Dr. Yanming Li, Mr. Rodrigo José De Andrade Vieira, Mr. Iñaki Bravo and
many others.
I would like to thank TechSolve for providing data that was used in this research work,
and in particular I would like to thank Dr. Radu Pavel for his collaboration.
Part of the experimental work of this research was sponsored by HIWIN, and, in this
respect I would like to thank Dr. Lawrence Huang, Dr. Jerry Chiu, Mr. Tereance Yu, Ms.
Julia Wen, Mr. Omer Hung, Mr. Wayne Chu and Mr. Howard Tsai.
I would like to thank my parents, my husband and his family, my friends Ms. Roxie
Higgins and Ms. Nancy Boss for their great support for my study and my life.

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TABLE OF CONTENTS

ABSTRACT .................................................................................................................................. II
ACKNOWLEDGMENTS ............................................................................................................... V
CHAPTER 1: INTRODUCTION ................................................................................................. 1
1.1 INTRODUCTION ............................................................................................................................................. 1
1.2 FUNDAMENTAL ISSUES ................................................................................................................................ 3
1.3 MOTIVATION AND CHALLENGES .................................................................................................................. 6
1.4 RESEARCH OBJECTIVE ................................................................................................................................ 7
1.5 CONTRIBUTIONS AND BROADER IMPACT ..................................................................................................... 8
1.6 DISSERTATION LAYOUT ............................................................................................................................... 9
CHAPTER 2: LITERATURE SURVEY ON PROGNOSTICS USING DEEP LEARNING AND
ACCELERATED LIFE TESTING METHOD ............................................................................... 12
2.1 PHM APPROACH FOR MACHINE LIFE PREDICTION .................................................................................. 13
2.2 FEATURE ENGINEERING AND FEATURE SELECTION ................................................................................. 16
2.2.1 FEATURE EXTRACTION .......................................................................................................................... 16
SENSOR-LESS TECHNIQUES ................................................................................................................................... 16
SENSOR-RICH TECHNIQUES ................................................................................................................................... 19
2.2.2 FEATURE SELECTION ............................................................................................................................ 21
CHOOSE DIAGNOSTIC FEATURES .......................................................................................................................... 21
CHOOSING PROGNOSTIC FEATURES ..................................................................................................................... 22
2.3 DEEP LEARNING METHODOLOGY .............................................................................................................. 26
WHAT IS DEEP LEARNING? ..................................................................................................................................... 26
WHY NEED DEEP LEARNING IN PHM? ................................................................................................................... 29
2.4 PROGNOSTIC METHODS FOR MACHINERY SYSTEMS ............................................................................... 31
2.5 ACCELERATED LIFE TESTING (ALT) ......................................................................................................... 35
2.5.1 ACCELERATED LIFE TESTING (ALT) MODELING UNDER CONSTANT STRESS CONDITIONS ............... 36
2.5.2 TIME-VARYING STRESS ACCELERATED LIFE TESTING ......................................................................... 44
2.5.3 ACCELERATED DEGRADATION TEST (ADT) ......................................................................................... 46
CHAPTER 3: INTEGRATING DEEP LEARNING METHOD AND ACCELERATED
DEGRADATION TESTING IN MACHINE PROGNOSTICS ....................................................... 52
3.1 PROBLEM STATEMENT AND ASSUMPTIONS .............................................................................................. 52
3.2 HYPOTHESIS .............................................................................................................................................. 56
3.3 DEEP LEARNING ENABLED FEATURE EXTRACTION ................................................................................... 58
3.3.1 OVERVIEW OF RESTRICTED BOLTZMANN MACHINES (RBMS) ............................................................ 58
3.3.2 RESTRICTED BOLTZMANN MACHINES WITH REGULARIZATION TERMS ................................................ 64
PROGNOSTICS REGULARIZATION ........................................................................................................................... 64
BAYESIAN REGULARIZATION .................................................................................................................................. 67
OTHER REGULARIZATIONS ..................................................................................................................................... 68
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SUMMARY ............................................................................................................................................................... 69
3.3.3 MIXED-VARIATE RESTRICTED BOLTZMANN MACHINES (MV-RBMS) .................................................. 70
MIXED-VARIATES RBMS INPUT TYPES .................................................................................................................. 70
REGIME MATRIX VARIATES .................................................................................................................................... 71
REGIME MATRIX BASED RBMS (RM-RBMS) ........................................................................................................ 72
3.3.4 FEATURE CONTRIBUTION ...................................................................................................................... 78
3.4 STEP-STRESS ACCELERATED DEGRADATION TESTS WITH PHM TECHNIQUES ...................................... 79
3.4.1 OVERVIEW OF STEP-STRESS ADT MODELING .................................................................................... 79
3.4.2 DEGRADATION MODELING AND TIME CONVERSION ............................................................................. 82
3.4.3 THEORETIC BALL SCREW LIFE CALCULATION ...................................................................................... 86
3.5 FRAMEWORK OF DEEP LEARNING BASED MACHINE LIFE PREDICTION UNDER CONSTANT STRESS
ACCELERATED DEGRADATION TESTS .................................................................................................................... 88
3.5.1 OVERALL METHODOLOGY OF APPLYING ENHANCED RBM WITH PROGNOSABILITY REGULARIZATION
ON MACHINE LIFE PREDICTION .............................................................................................................................. 88
3.5.2 BUILD HEALTH VALUE TRAJECTORY LIBRARY ...................................................................................... 90
3.5.3 SIMILARITY-BASED LIFE PREDICTION .................................................................................................... 91
3.6 FRAMEWORK OF DEEP LEARNING BASED ACCELERATED PROGNOSTICS AND HEALTH ASSESSMENT
USING STEP-STRESS ADT METHOD ..................................................................................................................... 93
CHAPTER 4: CASE STUDY–VALIDATION OF ENHANCED RBM WITH PROGNOSABILITY
REGULARIZATION FOR BEARING HEALTH ASSESSMENT ................................................. 96
4.1 INTRODUCTION ........................................................................................................................................... 96
4.2 TEST RIG SETUP AND DATA DESCRIPTION ............................................................................................... 97
4.3 DATA PROCESSING METHODS .................................................................................................................. 99
4.3.1 FEATURE PREPARATION ....................................................................................................................... 99
4.3.2 APPLY ENHANCED RBM TO FEATURE EXTRACTION .......................................................................... 100
4.4 UNDERSTANDING OF THE DEEP LEARNED FEATURES ............................................................................ 102
4.5 DISCUSSION OF RESULTS ....................................................................................................................... 104
CHAPTER 5: CASE STUDY–ENHANCED RBM BASED PROGNOSTICS FOR BEARING
LIFE PREDICTION ................................................................................................................... 105
5.1 INTRODUCTION ......................................................................................................................................... 105
5.2 TEST RIG SETUP AND DATA DESCRIPTION ............................................................................................. 105
5.3 HEALTH ASSESSMENT COMPARISON ...................................................................................................... 108
5.4 SCORE AND RESULTS .............................................................................................................................. 109
5.5 DISCUSSION OF RESULTS ....................................................................................................................... 112
CHAPTER 6: CASE STUDY–STEP-STRESS ADT OF LINEAR MOTION SYSTEM BASED
ON CATEGORICAL REGIME RBM METHOD ......................................................................... 114
6.1 INTRODUCTION ......................................................................................................................................... 115
6.2 TEST-BED SETUP AND DATA DESCRIPTION ............................................................................................ 116
6.3 DATA PROCESSING METHODS ................................................................................................................ 119
6.3.1 DATA VISUALIZATION AND VALIDATION ............................................................................................... 119
6.3.2 CONDITION MONITORING FEATURE EXTRACTION .............................................................................. 122
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6.3.3 FEATURE ENHANCEMENT WITH RM-RBM ......................................................................................... 124
6.3.4 HEALTH ASSESSMENT WITH RM-RBM FEATURES ............................................................................ 125
6.3.5 SSADT MODELING AND RUL PREDICTION RESULTS ........................................................................ 127
6.4 UNDERSTANDING OF THE DEEP LEARNED FEATURES IN LINEAR MOTION SYSTEM APPLICATION ........ 131
6.5 DISCUSSION OF RESULTS ....................................................................................................................... 132
6.6 APPLICATION AND DEMO ......................................................................................................................... 133
CHAPTER 7: CONCLUSIONS AND FUTURE WORK .......................................................... 134
7.1 CONCLUSION ........................................................................................................................................... 134
7.2 FUTURE WORK ........................................................................................................................................ 135
REFERENCE ........................................................................................................................... 137

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