ABSTRACT Title of Document: PROGNOSTICS OF POLYMER POSITIVE TEMPERATURE COEFFICIENT RESETTABLE FUSES Shunfeng Cheng, Doctor of Philosophy (Ph.D.), 2011 Directed By: Professor Michael G. Pecht Department of Mechanical Engineering Polymer positive-temperature-coefficient (PPTC) resettable fuse has been used to circuit-protection designs in computers, automotive circuits, telecommunication devices, and medical devices. PPTC resettable fuse can trip from low resistance to high resistance under over-current conditions. The increase in the resistance decreases the current and protects the circuit. After the abnormal current is removed, and/or power is switched off, the fuse resets to low resistance stage, and can be continuously operated in the circuit. The resettable fuse degrades with the operations resulting in loss or abnormal function of the protection of circuit. This thesis is focused on the prognostics methods for resettable fuses to provide an advance warning of failure and to predict the remaining useful life. The failure precursor parameters are determined first by systematic analysis using failure modes, mechanisms, and effects analysis (FMMEA) followed by a series of experiments to verify these parameters. Then the causes of the observed failures are determined by failure analyses, including the analyses of interconnections between different parts, the microstructures of the polymer composite, the properties (such as crystallinity) of the polymer composite, and the coefficient of thermal expansion (CTE) of different parts. The revealed failure causes include the cracks and gaps between different parts, the agglomerations of the carbon black particles, the change in crystallinity of the polymer composite, and the CTE-mismatches between different parts. Cross validation (CV) sequential probability ratio test (CVSPRT) is developed to detect anomalies. CV methods are introduced into SPRT to determine the model parameters without the need of experience and reduce the false and missed alarms. A moving window training updating based dynamic model parameter optimization (MW-DMPO) n-steps-ahead prognostics method is developed to predict the failure. MW methods update the training data for prediction models by a moving window to contain the latest degradation information/data and improve the prediction accuracy. For each updating of the training data, the model parameters for data-trending model are updated dynamically. Based on the developed MW-DMPO method, a MW cross validation support vector regression (MW-CVSVR) n-steps-ahead prediction is developed to predict failures of PPTC resettable fuses in this thesis. The cross validation method is used to determine the proper SVR model parameters. The CVSPRT anomaly detection method and MW-DMPO n-steps-ahead prognostics method developed in this thesis can be extended as general methods for anomaly detection and failure prediction. PROGNOSTICS OF POLYMER POSITIVE TEMPERATURE COEFFICIENT RESETTABLE FUSES By Shunfeng Cheng Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2011 Advisory Committee: Professor Michael G. Pecht (Advisor), Chair Professor Donald Barker Professor Bilal Ayyub (Dean’s representative) Professor Peter Sandborn Associate Professor David Bigio © Copyright by Shunfeng Cheng 2011 Dedication This dissertation is dedicated to my lovely wife, Haiyan Zhang, my parents-in-law, Tiehua Zhang and Guangping Wei, and my father, An Cheng, who support me all the time. Especially, this dissertation is a memorial to my mother, Yaogui Zhang, who always gave me her endless love. ii Acknowledgements First, I would like to express my gratitude and respect to Prof. Michael Pecht for his guidance in this interesting dissertation topic and challenging me to set a higher standard. I appreciate him for his continuous support towards the progress of this work and the valuable help on my writing and presenting skills. I would also like to thank Prof. Donald Barker, Prof. Bilal Ayyub, Prof. Peter Sandborn, and Prof. David Bigio for serving on my dissertation committee and providing valuable suggestions. I would like to especially thank Dr. Michael Azarian and Dr. Diganta Das for their insightful advice to this work. I further thank the Prognostics and Health Management Consortium in Center for Advanced Life Cycle Engineering (CALCE) for its support. I would like to thank Dr. Michael Osterman, Dr. Nikhil Lakhkar, Dr. Alex Coppe, Mr. Bhanu Sood, Mr. Anshul Shrivastava, and Mr. Mark Zimmerman for their supports on my experiments and help on this thesis. I would like to thank my friends and colleagues at CALCE including Dr. Nikhil Vichare, Dr. Jie Gu, Dr. Lei Nie, Dr. Daeil Kwon, Dr. Sachin Kumar, Dr. Nishad Patil, Weiqiang Wang, Wei He, Xiaofei He, Jin Tian, Gilbert Haddad, Edwin Sutrisno, Vasilis Sotiris, Hyunseok Oh, Sandeep Menon, Mohammed Alam, and Sony Mathew for their help during my graduate experience. I would like to thank Dr. Larry Lai in Nano Center, Dr. Xin Zhang in Material Science, and Dr. Peter Zavalij in Department of Chemistry and Biochemistry for their help on the analyses of samples. Finally, I would like to extend very special thanks to my wife Haiyan Zhang and my family for their constant support and encouragement towards my work. iii Table of Contents Dedication .................................................................................................................ii Acknowledgements ................................................................................................. iii List of Publications ................................................................................................... vi List of Tables............................................................................................................ ix List of Figures ........................................................................................................... x Chapter 1: Introduction ........................................................................................ 1 1.1 Positive Temperature Coefficient Effect ..................................................... 1 1.2 Operation of PPTC Resettable Fuses .......................................................... 2 1.3 Motivation ................................................................................................. 4 1.4 Problem Statement and Research Work ...................................................... 5 1.5 Overview of Thesis .................................................................................... 6 Chapter 2: Literature Review ............................................................................... 7 2.1 Operational Principle of PPTC Resettable Fuses......................................... 7 2.2 Electrical Characteristics of PPTC Resettable Fuses ................................. 11 2.3 Factors Affecting Characteristics of PPTC Resettable Fuses ..................... 13 2.4 Reliability and Testing of PPTC Resettable Fuses .................................... 17 2.5 Prognostics Methods ................................................................................ 19 2.6 Summary .................................................................................................. 22 Chapter 3: Failure Precursors of PPTC Resettable Fuses .................................... 23 3.1 Potential Failure Precursor Parameters of PPTC Resettable Fuses ............ 23 3.2 Experiments and Results .......................................................................... 29 3.2.1 Trip cycle test ....................................................................................... 29 3.2.2 Aging test ............................................................................................. 32 3.2.3 Results ................................................................................................. 33 3.3 Summary .................................................................................................. 41 Chapter 4: Determination of Failure Causes ....................................................... 42 4.1 Failure Analysis Methods ......................................................................... 42 4.2 Analysis Results ....................................................................................... 45 4.2.1 IR test results........................................................................................ 45 4.2.2 Interconnection analysis results ............................................................ 48 4.2.3 Microstructure analysis results ............................................................. 52 4.2.4 DSC analysis results ............................................................................. 56 4.2.5 CTE test results .................................................................................... 59 4.3 Causes for Changes in Parameters ............................................................ 60 4.3.1 Causes for changes in trip time and surface temperature ....................... 61 4.3.2 Causes for changes in resistance after reset ........................................... 64 4.4 Summary .................................................................................................. 65 Chapter 5: Anomaly Detection Using Cross Validation Sequential Probability Ratio Test (CVSPRT) .............................................................................................. 66 5. 1 Wald’s