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PHM Topics
PHM Publication
The Reliability Society

Technical program

Special Sessions Information:

  • PHM for Transportation. Focusing on PHM applications of sustainable transportation, with a goal of integrating Operational Technology, Information Technology, and Engineering Technology for intelligent transportation systems through effective PHM implementation. Organizer: Dr. Janet Lin, Luleå University of Technology (LTU), Luleå, Sweden

  • Advanced Signal Processing for PHM. Focusing on the advancement of signal processing methods and applications in PHM. Organizers: Drs. Zhiliang Liu, University of Electronic and Science Technology of China; and Yongbo Li,, Northwestern Polytechnical University, China

  • Some Recent PHM Advances and Applications in Aerospace Engineering. Focusing on the recent development of methods and applications of PHM in the aerospace industry. Organizers: Dr. Shuguang Song, The Boeing Company, USA; Prof. Min Xie,, City University of Hong Kong, Hong Kong.

  • AI for PHM: Focusing on the latest developments and implementations of artificial intelligence and data mining methods for PHM. Organizer: Dr. Yan-Fu Li,, Tsinghua University, Beijing, China.

  • PHM for ultra-high reliability electronics: Focusing on the innovative PHM methods which could minimize the built-in tester’s effects to reliability of ultra-high reliability electronics. Organizer: Dr. Gang Dai,, Microsystem and Terahertz Research Center, China Academy of Engineering Physics, Chengdu, China.

  • PHM for mission critical complex system: Focusing on new principles, theories, and applications as well as the design of systemic PHM for mission critical complex system. Organizer: Dr. Xiaojian Yi,, and Hui-na Mu, and Hai-ping Dong, China North Vehicle Research Institute, Beijing Institute of Technology, Beijing, China

Panel Talk:

  • Deep Learning in Prognostic and Health Management: Prognostics and health management (PHM) is a multi-disciplinary research area that provides efficient and robust solutions for managing the health of machines mainly in an industrial environment. For past few years, PHM has been catering the needs of engineering community in industries and academics for developing methodologies to achieve the objectives of PHM such as reliability, maintainability, safety, and affordability of machines in an industrial environment. In the recent years, a large number of machine learning based approaches have been suggested to achieve the objectives of the PHM. However, with the help of deep learning strategies, which is an advanced learning technique, the performance of machine learning/computational intelligence based models can be greatly improved for better PHM of the machines under uncertain and noisy environment. Some of the deep learning based models, beings applied in PHM are deep neural networks (DNN), deep belief network (DBN), deep Boltzmann machine (DBM) and deep fuzzy network (DFN) etc.

Dr. Nishchal K. Verma (SM’13) is an Associate Professor in Dept. of Electrical Engineering, Indian Institute of Technology Kanpur, India. He received his PhD in Electrical Engineering from Indian Institute of Technology Delhi, India. He worked as Post-Doctoral Research Fellow in Center for Integrative and Translational Genomics, University of Tennessee, Memphis, TN 38163 USA and Post-Doctoral Research Associate in Department of Computer Science, Louisiana Tech University, Ruston LA 71270 USA. He was awarded Devendra Shukla Young Faculty Research Fellowship by Indian Institute of Technology Kanpur, India for year 2013-16 and recently, he is awarded Achiever award by Institution of Engineers at Jodhpur, India on Engineers day (Sept. 15th, 2017).
His research interests include Cognitive Science, Big Data, Bioinformatics, Intelligent Data Mining Algorithms and Applications, Computer Vision and Image Processing, Brain Computer/Machine Interface, Machine Intelligence, Intelligent Informatics, Smart Grid, Intelligent Fault Diagnosis Systems, Prognosis and Health Management, Soft-Computing in Modelling and Control, Internet of Things/ Cyber Physical Systems, Intelligent Agents and their Applications, Fuzzy Logic and Controllers, Deep Learning of Neural and Fuzzy Networks, Machine Learning Algorithms, Computational Intelligence. He authored and co-authored more than 190 research papers.
Dr. Verma is an IETE Fellow. He is currently serving as an Editor of the IETE Technical Review Journal, an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Transactions of the Institute of Measurement and Control, U.K., Guest Editor of special issue on “Computational Intelligence” of International Journal of Swarm Intelligence, Guest Editor of special issue on “Computational Intelligence: Theories, Applications and Future Directions” of International Journal of Artificial Intelligence and Soft Computing and an Editorial Board Member for several journals and conferences.


If you would like to organize a special paper session or panel, please send a summary about the proposed session/ panel as well as potential speakers to the Program Chair Dr. Steven Li, Thank you for your contributions and look forward to seeing you in Seattle in June 2018.

Last site update: 2017/03/06
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