PHM for Typical Mechanical Components
Focusing on PHM applications of the challenges of PHM in typical and critical mechanical components, with topics in, but not limited to, prediction method of mechanical components, failure mechanism analysis of mechanical components, performance degradation modeling, reliability, diagnostics, and prognostics of rolling bearings, gears as well as fault feature extraction and diagnostic method, other relevant PHM topics related to mechanical components and systems.
Dr. Zhinong Li, Nanchang Hangkong University, Nanchang, China. Email
PHM for Gas Turbine Engines
Gas turbines have been widely used in the aerospace and shipbuilding industry, oil and gas pipeline transportation, and industrial power plants due to its excellent performances. As the power heart for these mission critical applications, safe and stable operation of gas turbines is the key. In the operation of the gas turbines, in addition to the harsh operating conditions of high temperature, high pressure, high rotational speed and high mechanical stress and thermal stress inside the engine, it may also suffer from surrounding polluted environmental conditions, and its main components, such as compressors, combustion chambers and turbines, will produce a variety of performance degradation or damage with increasing operating time, and easily lead to various serious failures. In order to improve the reliability and availability of the equipment, while maximizing the service life and reducing the operation & maintenance costs, the users need to adopt the corresponding maintenance strategy according to the actual performance and health status of the engine through monitoring, diagnosis and prognosis means, and condition-based maintenance (CBM).
This session covers research in turbomachinery fault diagnosis & prognosis, and welcomes papers with topics in, but not limited to, fault diagnosis based on acoustic signal processing, fault diagnosis based on vibration signal processing, fault diagnosis and prognosis based on thermodynamic model, fault diagnosis based on special sensors, fault diagnosis and prognosis based on artificial intelligence, Software development for fault diagnosis and prognosis, Hardware & software for remote monitoring and diagnosis (RM&D).
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 increased capacity, and more reliable and robust for existing transport systems. 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 system, aerospace, etc. Techniques and approaches used, results obtained, and lessons learned can be included to share experiences 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
AI for PHM
Prognostics and health management (PHM) aims at diagnosing the ongoing fault, predicting the remaining useful life (RUL) before failure and scheduling the maintenance/repair action cost-effectively. Given the rapid development of sensor and data transmission technology, big-data becomes one valuable resource for PHM. In the era of big data, artificial intelligence (AI) techniques have gained increasing popularity due to their capabilities of solving large scale problems without much essential domain knowledge. AI has achieved huge success in the fields of the computer vision, natural language processing, game play, autonomous driving, as well as in PHM. For example, the deep learning methods have been applied for the RUL prediction of rotating machinery, battery, etc. In general, the application of AI techniques in the field of PHM can generate enormous benefits for PHM community, both in academia and industry. This special session “AI for PHM” is dedicated for a platform of the exchange of the cutting-edge developments and implementations of AI and data mining methods for PHM.
Advanced Sensing, Data Fusion and Fault Diagnostics
Structural and machinery health monitoring relies heavily on advanced sensing techniques and corresponding information processing engine. Versatile sensing techniques can provide more insight into health conditions of mechanical and electrical systems. Recently, acoustic sensors, optic fibers, infrared sensors, laser sensors, LiDAR and MEMS are all investigated for PHM applications. Furthermore, information from distributed and/or multi-parameter sources requires sophisticated algorithms for feature extraction, multisensory data fusion and intelligent decision making. This session covers research in the above directions, and welcomes original papers with topics in, but not limited to, principles, methodologies and related applications on sensor development, feature extraction, data fusion, dynamic modeling, and signal processing techniques for fault diagnostics.
Fault Injecting and Modeling in PHM
This proposed session is to share knowledge about the fault injecting and modeling in PHM, focusing topics may include, but not limited to: Computational algorithms and modeling for typical fault mode in mechanical components, newly developed techniques to inject fault in experimental platforms, uncertainties and reliability analyses, performance evaluation criteria, failure mechanism analysis for bearings, gears, and joints, fault feature extraction and diagnostic method, other topics related to fault injecting and modeling in PHM for mechanical components and systems.
Dr. Guanghan Bai, National University of Defense Technology, China. Email
Fault Diagnosis, Prognosis, and Health Management for Aircraft Electrical System
The development of science and technology has improved the automation level and increased the number of electrical equipment on aircraft significantly. In addition, the development of more electric and all electric Aircraft also makes the equipment and electrical system on aircraft more and more complex. With this increasing demand of electric energy in aircraft, the requirement for reliability, safety, and maintainability has become demanding. By integrating advanced sensor technology and various intelligent algorithms and modeling tools, prognosis and health management (PHM) technology are used to carry out system state detection, fault diagnosis, prognosis, as well as residual life prediction. PHM also provides suggestions for aircraft electrical system reconfiguration and control in advance, and optimizes maintenance strategies. Moreover, PHM can comprehensively monitor the health status of critical components and the entire aircraft system, which facilitates self-repairing, task relegation, and preparation of necessary maintenance resources in advance, in order to reduce the life-cycle cost. Thus, it is of great significance to carry out the research of PHM for aircraft electrical system.
This session covers research in reliability, fault diagnosis & prognosis, and health management for aircraft electrical systems, and welcomes papers with PHM related topics in, but not limited to: generators, distributors, converters, cables, motors, loads and other components, as well as primary distribution system, secondary distribution system, and aircraft electrical system, etc.
Design and Integration of IVHM for Commercial Aircraft
Commercial aircraft is the emerging field in the applications of PHM technology. PHM technology plays an important role in the safety, economy, and efficient operation for modern commercial aircraft, and has become a critical method to enhance the market competitiveness of new generation commercial aircraft. IVHM of commercial aircraft is the integrated integration and engineering application of the PHM elements within its internal system and structure. How to implement the PHM technology for commercial aircraft, and implement the requirement and function of IVHM into the design of specific aircraft system and structure, and realize the condition monitoring, failure detection, trending and condition-based maintenance through system integration is a common concern of academic and engineering community. It is of great significance to carry out academic exchanges and discussions in this field for promoting the cooperation of industry-university-research and upgrading of PHM technology for commercial aircraft research and development.
The purpose of this special session is to carry out a series of academic discussions on the design and integration of PHM technology for commercial aircraft. The contents include but not limited to:
Dr. Qiang Guo, Research fellow, Shanghai Aircraft Design and Research Institute, COMAC, China. Email
Dr. Peng Wang, Research fellow, Civil Aviation University of China, China.
Safety and Reliability in Water Transport System
Water transport accounts for around 90% of international trade in China because it has the distinguishing characteristics of long transport distance, high volume and low emission. Unwanted and catastrophic accidents occur worldwide, for instances, Costa Concordia (2012), Seoel (2014), and Eastern Star (2016). Therefore, the safety and the reliability of water transport system are still key issues and deserve more attention for risk mitigation. Moreover, with the development of emerging technologies, some emerging safety issues need to be addressed. For examples, the safety and reliability in different regions (Arctic areas), the safety and reliability of advanced ships (using pure electric propulsion, autonomous ships), the reliability and safety of engineering and technology for large maritime systems (offshore wind farms, offshore installations).
This session covers research in risk analysis, decision-making and human reliability analysis for water transport systems. The contents include, but not limited to:
Prof. Xinping Yan, Wuhan University of Technology, China Email
Dr. Tsz Leung Yip, Hong Kong Polytechnic University, Hong Kong Email
Prof. Di Zhang, Wuhan University of Technology, China Email
Dr. Bing Wu, Wuhan University of Technology, China Email
PHM for Marine Equipment and Facilities
Marine equipment and facilities, including the marine energy converter, marine platform, AUV, ROV, surface/subsurface buoy, glider and other oceanographic instruments, work in harsh environment. Those rough operating conditions of high salt spray, high humidity, low temperature and high pressure will cause rapid aging and failures of marine equipment. And the marine equipment cost a lot during deployment and maintenance. So their reliability and availability should be considered during the whole life cycle from design, manufacturing and service. PHM methods and technologies provide a lot of knowledge to improve the reliability and availability of the marine equipment, while maximizing the service life and reducing the operation & maintenance costs. Both the researchers and the users should focus on the monitoring, diagnosis and prognosis technologies for not only the electrical system but also the structures and mechanical components for those marine equipment.
The purpose of this special session is to carry out a series of academic discussions on the PHM technology for marine equipment. The contents include but not limited to: Fault Diagnosis, Prognosis, and Health Management for marine equipment and facilities, Monitoring and Sensor Technology, Signal processing, Thermal behavior, Modeling of Failure Detection, PHM Design, integration and verification technology, Modeling of aging mechanism, Remaining Useful Life prediction, Condition-Based Maintenance technology, dynamics, vibration and control for marine equipment and facilities, performance estimation and optimization of marine system, Hardware & software for remote monitoring and diagnosis and related onshore & offshore PHM topics for all kind of components, systems and equipment in marine applications.
Dr. Liqiang Zhang, Ocean University of China, Qingdao, China. Email
Dr. Ming Li, Ocean University of China, Qingdao, China. Email
Dr. Zongyu Chang, Ocean University of China, Qingdao, China. Email
PHM Based on Time Series Analysis
PHM is essentially an analysis of time series. The vibration information of the equipment is collected as a time series, by dissecting the change of long-term trend and stochastic changes of equipment operation status, realizing the identification of the initial weak fault and assessment of life prediction. The content involves long-range dependence, short-range dependence, chaos, fractal, wavelet, entropy, and spectral kurtosis, etc.
Wanqing Song, Shanghai University of Engineering Science, China. Email
Application of PHM in Aviation
PHM technology is one of the core technologies in aviation industry. Researches on PHM technology are of great significance so as to ensure flight safety of aircraft, and improve maintenance efficiency and mission success rate of aircraft. In order to further promote the development of PHM technology, and to improve the application of condition monitoring, fault diagnosis, life prediction and health management technology in aviation industry, a special session entitled "Application of PHM in Aviation" will be organized in PHM-2019, Qingdao. The topics of this session include, but not limited to:
Dr. Yong Shen, Shanghai Aerospace Measurement and Control Technology Research Institute, China.