Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment
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Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment 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 Jianshe Feng June 2020 B.Sc. in Mechanical Engineering, Tongji University (2012) M.Sc. in Mechatronics Engineering, Zhejiang University (2015) Committee: Prof. Jay Lee (Chair) Prof. Jing Shi Prof. Manish Kumar Prof. Thomas Huston Dr. Hossein Davari Dr. Zongchang Liu ii Abstract Prognostics and health management (PHM) has gradually become an essential technique to improve the availability and efficiency of a complex system. With the rapid advance- ment of sensor technology and communication technology, a huge amount of real-time data are generated from various industrial applications, which brings new challenges to PHM in the context of big data streams. On one hand, high-volume stream data places a heavy demand on data storage, communication, and PHM modeling. On the other hand, continuous change and drift are essential properties of stream data in an evolving environment, which requires the PHM model to be capable to capture the new information in stream data adaptively, efficiently and continuously. This research proposes a systematic methodology to develop an effective online learning PHM with adaptive sampling techniques to fuse information from continuous stream data. An adaptive sample selection strategy is developed so that the representative samples can be effectively selected in both off-line and online environment. In addition, various data-driven models, including probabilistic models, Bayesian algorithms, incremental methods, and ensemble algorithms, are employed and integrated into the proposed methodology for model establishment and updating with important samples selected from streaming sequence. Finally, the effectiveness of proposed systematic methodol- ogy is validated with four typical industrial applications including power forecasting of a combined cycle power plant, fault detection of hard disk drive, virtual metrology in semiconductor manufacturing processes, and prognosis of battery state of capacity. iii The result comparison between the proposed methodology and state-of-art benchmark methods indicates that the proposed methodology is capable to build an adaptive PHM with sustainable performance to deal with dynamic issues in processes, which provides a promising way to prolong the PHM model lifetime after implementation. Keywords: adaptive PHM; sample selection; sample importance test; online model- ing; sequential model updating iv To my beloved wife and family. v vi Declaration I hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and Acknowledgements. This dissertation contains fewer than 40,000 words including appendices, bibliography, footnotes, tables, and equations and has fewer than 100 figures. Jianshe Feng June 2020 vii viii Acknowledgements First of all, I would like to express my foremost gratitude to my advisor Prof. Jay Lee, for his continuous support and insightful guidance throughout my PhD study. Without his support, I would never have had the chance to accomplish this research in an excellent research environment. My sincere gratitude also goes to my committee members: Prof. Jing Shi, Prof. Manish Kumar, Prof. Thomas Huston, Dr. Hossein Davari, and Dr. Zongchang Liu for their valuable feedback and helpful guidance during my research. I would like to thank Dr. Yilu Zhang and Dr. Xinyu Du from GM R&D center for the internship opportunity to work on autonomous vehicle diagnosis and prognosis project. I would like to thank Dr. Zwich Tang and Ms. Aisha Yousuf from Eaton for the internship opportunity to work on electricity grid fault isolation and localization project. These internship experiences gave me the chances to put my research into a wider context, investigate how it fits into a bigger picture, and inspire new ideas for my research. I would like to thank all the collaborators from various companies including Applied Materials, Plastic Omnium, Shanghai Electric, PWC, Kinpo-ACCL, etc. Special thanks given to Dr. James Moyne from University of Michigan and Applied Global Services Group. His expertise and insights in semiconductor manufacturing inspired me a lot during our collaboration. I would like to give my thanks to all in IMS center for the collaborative work and great support. They are Dr. Hossein Davari Ardakani, Dr. Wenjing Jin, Dr. Chao Jin, Dr. Ann Kao, Dr. Zongchang Liu, Dr. Zhe Shi, Dr. Yuan Di, Dr. Xiaodong Jia, Dr. ix Shaojie Wang, Dr. Jaskaran Singh, Dr. Xiang Li, Mr. Behrad Bagheri, Mr. Pin Li, Mr. Bin Huang, Mr. Qibo Yang, Ms. Laura Pahren, Ms. Sherry Cai, Mr. Honghao Dai, Mr. Runfeng Tian, Mr. Yuan-Ming Hsu, Mr. Vibhor Pandhare, Mr. Cyrus Azamfar, Mr. Himanshu Grover, Mr. Feng Zhu, Mr. Wenzhe Li, Mr. Fei Li, Ms. Qian Yang, Miss Yinglu Wang, Mr. Shaojie Yang, Mr. Shahin Siahpour, Ms. Marcella Miller, Dr. Ming Zhao, Dr. Yibing Yin, Dr. Yuan-Jen Chang, Dr. Huijie Mao, Mr. Yubin Pan, Mr. Hongsheng Yan, Dr. Mingqiang Zhu, Dr. Jianhai Yue, Dr. Yang Tang, Dr. Zhongwei Wang, and many others. I appreciate Mr. Patrick Brown and Mr. Michael Lyons for the grateful organization and administration in past years. It is my honor to be an IMSer and work with you guys! Finally, I would like to thank the whole big family, especially my parents Yujie Feng ( ¯玉p : Féng Yùjié) and Xian An ( 安$ : An¯ Xián), and my love Dantong. The consistent supports and unwavering love from my big family have always given me the strength to get through tough times on this journey. x Table of contents List of figures xv List of tables xxi Nomenclature xxiii 1 Introduction1 2 Literature Review and Related Works5 2.1 Overview of Prognostics and Health Management . .5 2.2 Recent Adaptive PHM Practices . .7 2.3 Related Research Topics . .9 2.4 Challenges and Research Gaps . 28 3 Development of Adaptive PHM Methodology 31 3.1 Framework of Adaptive PHM Methodology . 31 3.2 Sample Selection Strategy of Adaptive PHM . 34 3.3 Models of Adaptive PHM . 44 3.3.1 Bayesian Methods . 46 3.3.2 Adaptive Ensemble Methods . 48 3.3.3 Neural Nets . 49 3.3.4 Online Kernel Methods . 51 xi Table of contents 3.4 Off-line Sample Selection and Modeling Techniques . 52 3.5 On-line Sample Selection and Modeling Techniques . 53 3.6 Justification of Sample Importance Test . 54 3.7 An Intuitive Case of Sample Selection and Modeling . 57 3.7.1 Background . 58 3.7.2 Design of Experiments . 59 3.7.3 Results and Discussions . 59 3.7.4 Summary . 63 4 Case Studies 65 4.1 Overview of Case Studies . 65 4.2 Case Study I - Hard Disk Drive Online Fault Detection . 66 4.2.1 Background . 66 4.2.2 Data Description . 68 4.2.3 Methodology . 68 4.2.4 Results and Discussions . 73 4.2.5 Summary . 81 4.3 Case Study II – Adaptive Virtual Metrology of CMP Process . 82 4.3.1 Background . 82 4.3.2 CMP Process Introduction . 86 4.3.3 Methodology . 88 4.3.4 Results and Discussions . 100 4.3.5 Summary . 112 4.4 Case Study III – Battery Capacity Prognosis . 113 4.4.1 Background . 113 4.4.2 Methodology . 114 4.4.3 Data Description and Experiment Design . 121 xii Table of contents 4.4.4 Results and Discussions . 124 4.4.5 Summary . 128 5 Conclusions and Future Work 131 5.1 Conclusions . 131 5.2 Future Work . 134 References 137 Appendix A List of Publications in PhD Study 163 xiii xiv List of figures 2.1 General PHM analytics approach . .8 2.2 Traditional PHM modeling and deployment . 10 2.3 An illustration of two different types of concept drift in the context of classification problems. The samples with different colors are two classes, and the red dash line is the decision boundary (a) original observed data (which can be seen as offline available data); (b) the observed data with real concept drift; (c) the observed data with virtual concept drift. 12 2.4 An illustration of change points in a time series, where scatter points are time series samples, and the horizontal lines indicate separate working regimes (which are different rotating speeds in this illustration). 13 2.5 An illustration of incremental learning process based o streaming samples (modified based on Žliobait˙e(2010))..................... 16 2.6 Different learning mechanism between traditional machine learning and transfer learning (Copyright: Pan and Yang(2009)) . 20 2.6 Intuitive cases for difference learning method comparison . 25 3.1 Proposed adaptive PHM framework for offline initialization and online evolving. 33 3.2 Sample Importance test (SIT) . 35 xv List of figures 3.3 Alignment illustration: associate each element of sequence X to one or more elements of sequence Y and vice-versa, arrows show the desirable points of alignment. 38 3.4 DTW path illustration. 38 3.5 Data-based sample selection . 40 3.6 Model-based sample selection . 42 3.7 An illustration of dynamic weighted ensemble model. 49 3.8 Offline sample selection and modeling . 54 3.9 On-line sample selection and modeling . 56 3.10 Illustrations of SIT . 57 3.11 Diagram of a combined cycle power plant (CCPP) . 58 3.12 Flow chart of power prediction of CCPP . 60 3.13 Test settings with different train-test ratios .