PHM for transportation
The sustainable transports of the future will depend on smart and innovative solutions to deal with the increased volume of passengers and goods, and while at the same time reduce the detrimental effects of transport on the environment and climate. Successful PHM will be a must for more effective and efficient maintenance, lower energy use and increase capacity, and more reliable and robust for existing transport system. Recently, for the successful development and implementation of PHM program, there is a spoken need of convergence of the Operational Technology (OT), Information Technology (IT) together with Engineering Technologies (ET). This special session solicits papers that present various industrial PHM applications of sustainable transportation, with a special focus on technology that enables convergence of OT and IT. These include but not limited to: maritime transport system, railway system (rolling stocks and/or infrastructure), road transport systems, aerospace, etc. Techniques and approaches used, results obtained, and lessons learned can be included to share experience with this session.
Dr. Janet (Jing) Lin, Luleå University of Technology, Sweden. Email
Dr. Baoping Cai, China University of Petroleum, China. Email
Dr. Liangwei Zhang, Dongguan University of Technology, China. Email
Dr. Haidong Shao, Hunan University, China. Email
PHM for Wind Turbine Operation and Maintenance
Wind turbine is a comprehensive product integrating electrical，mechanical, air mechanics and other disciplines, all parts of which are closely related. The maintenance level of wind turbine directly affects the amount of power generation and the level of economic benefits. The quality and maintenance efficiency of each component of the Wind turbine product also directly affect the operation and maintenance level of the Wind turbine.
This session covers the PHM research of Wind turbine operation and maintenance, and welcomes papers with PHM related topics in, but not limited to: Life prediction of parts, components pre replacement, fan state detection, fault diagnosis and prognosis, health management, maintenance strategy, etc.
Reliability Modeling for Various Special Types of Data
A challenging issue in reliability area is the reliability modeling for various special types of data such as highly censored data, interval-data (e.g., spare part consumption data), the data with covariates and the data with complex censoring pattern (e.g., dependent censoring). For such data, the classical reliability modeling methods might no longer be applicable and new modeling approaches are needed. This session focuses on this issue.
Mechanical Systems Reliability Enhancement through Vibration Analysis
Vibration has a significant impact on the reliability of mechanical equipment. An uncomfortable truth about modern mechanical systems, such as aircraft and marine ship, is that the vibration is excited by multi-field coupled loads. The more reliable and robust design of equipment involves vibration monitoring and analysis. Here, the vibration analysis is multi-physics-based interdisciplinary research, which brings new challenges to the reliability design for mechanical equipment. This session covers the aspects of experimental and computational vibration analysis methods for reliability enhancement, design and optimization of mechanical systems. These topics include, but not limited to, multi-physics-based vibration analysis models, emerging techniques for the design of mechanical systems, uncertain dynamic analysis and robust design. Presentations on theoretical and experimental work, as well as case studies, are welcome.
Industrial Big Data Mining and Deep Learning for PHM
With the rapid enhancement of computing capacity and the upcoming application of 5G communication infrastructure, the application of Industrial big data mining and deep learning technologies to prognostics and health monitoring (PHM) of machinery, is the focus of the next stage. However, the Industrial big data in Internet of Things is with a certain redundancy，complex and unstructured characteristic, it is a great challenge for analyzing and processing industrial big data in the application of PHM. Recently, deep learning has achieved great success on the value mining of the industrial big data, which include the discovery of new patterns and knowledge and the extraction of novel valuable information. This special session solicits papers that present various industrial PHM applications of machinery such as bearing, gearbox and hydraulic device, with a special focus on industrial big data mining and deep learning technologies. These include but not limited to: intelligent algorithms and modelling for anomaly detection, fault diagnosis and remaining useful life prediction of machinery.
Dr. Jun Wu, Huazhong University of Science and Technology, Wuhan, China. Email
Dr. Carman K.M. Lee, The Hong Kong Polytechnic University, Hong Kong, China. Email
Dr. Juncal Xu, Case Western Reserve University, Cleveland, OH, USA. Email
Dr. Yuanhang Wang, China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, China. Email
Energy Storage and Electrical Device Reliability
Energy storage devices, such as Li-ion batteries and ultracapacitors, have been increasingly used in electrical devices and grid system, which raises a challenge to reliability and safety modelling, degradation behavior investigation, and failure/degradation mechanism identification for energy storage and electrical devices, due to the specific working conditions, multiple influence factors, and big data. This session focuses on this issue.