#1 Application of information theory in the prognostics and health management of machines

Prognostics and health management (PHM) techniques have been rapidly advanced in recent years for promoting the productivity and reliability of large-scale engineering systems. This advancement is greatly impacted by the progress in information theory and computing technologies, evidenced by many published works in PHM fields such as Shannon entropy and its variants, Lempel ziv complexity. As statistical nonlinear measures, indices derived from information theories can characterize and quantify the machine health condition and its evolution in a continuous manner during its operation. Hence, information theory can be considered as a useful and reliable measure for develop and design novel prognostic and health management techniques for machineries.

Considering the aforementioned interest among the researchers, this special session aims to provide a platform to present high-quality original research on the latest developments of information theory and its application to the PHM of machines.

Organizers: Yongbo Li, Xianzhi Wang, Khandaker Noman

Information theory; Continuous health monitoring; Complexity measure;Entropy; Early fault detection;Remaining useful life;Fault diagnosis and prognosis;Data driven predictive maintenance;Intelligent maintenance;Condition based monitoring

#2 Deep Learning for High-end Equipment Health Diagnosis and Prognosis

With the rapid technological development, high-end equipment, such as high-speed train, helicopters, aero-engine, etc., has become increasingly complex. health diagnosis and prognosis play a significant role for their life-cycle management. In the recent decade, Artificial intelligence (AI) techniques, especially deep learning approaches, are the most powerful tools in advancing health diagnosis and prognosis of high-end equipment.

This special session focuses on advanced and innovative applications of deep learning for high-end equipment health diagnosis and prognosis. We welcome submissions on both theoretical innovations and real-world applications related to deep learning. The topics for this special session include but are not limited to the following:

● Deep learning enabled data augmentation technology for health diagnosis

● Explainable deep learning methods for health diagnosis and prognosis

● Deep transfer learning methods for health diagnosis and prognosis

Ruqiang Yan, Xi’an Jiaotong University, China, Email:yanruqiang@xjtu.edu.cn

#3 Cross-domain fault diagnosis methods for rotating machines

Modern machines are becoming more automatic and efficient, which has increasingly high requirements for the reliability and quality. To ensure the reliable operation of rotating machines, it has always been an issue of significance to comprehensively and accurately diagnose the latent faults of the machinery. In recent years, transfer learning has been widely used in various fields of rotating machinery fault diagnosis due to its ability to learn cross domain features. This special session is interested in articles on the latest research progress and achievements of cross domain fault diagnosis methods for rotating machines. Potential topics include but are not limited to the following:

● Advanced signal processing techniques for cross domain feature extraction;

● Transfer learning–based intelligent fault diagnosis of machines;

● Fault detection of machines under varying speed conditions;

● Advanced sensing and monitoring techniques under variable working condition;

● Fault diagnosis methods based on cross-domain adaptive;

● Sensor fusion techniques under different domain data.

● Applications of domain generalization techniques for fault classification of bearing and gear.

Prof. Haidong Shao, Hunan University, China, Email:hdshao@hun.edu.cn

Prof. Jinrui Wang, Shandong University of Science and Technology, China, Email:wangjinrui@sdust.edu.cn

Dr. Xiaoli Zhao, Nanjing University of Science and Technology, China, Email:xlzhao@njust.edu.cn

#4 Methods and applications in PHM considering missing information

In academic research and engineering practice of reliability and PHM, incomplete information and limited data quality are very common and troublesome problems. Typical examples include lifetime analysis considering censored and truncated data, degradation modeling with measurement error, maintenance decision optimization based on hidden semi-Markov decision process, etc. This special session is interested in articles on the latest research progress and achievements of methods and applications in PHM considering missing information, innovations incorporating engineering problems are particularly welcomed. Potential topics include but are not limited to the following:

● Lifetime analysis and reliability evaluation considering multiple censored and truncated data

● Degradation modeling with measurement error and/or outliers

● Hidden semi-Markov decision process and semi-Markov decision process

● Innovative applications of Expectation-Maximization algorithm

● Bayesian method/particle filter method

● Transfer learning–based methods for limited information;

● Engineering applications of missing information principle.

Prof. Zengqiang Jiang, Beijing Jiaotong University, China, Email:zqjiang@bjtu.edu.cn

Prof. Mingcheng E, Beijing Jiaotong University, China, Email:emch@bjtu.edu.cn

Prof. Qi Li, Beijing Jiaotong University, China, Email:liqi@bjtu.edu.cn

#5 PHM based on Digital Twin

A digital twin is a virtual model of a process, product or service. This pairing of the virtual and physical worlds allows analysis of data and monitoring of systems to head off problems before they even occur, prevent downtime, develop new opportunities and even plan for the future by using simulations. Smart components that use sensors to gather data about real-time status, working condition, or position are integrated with a physical item. This data help researchers build more accurate model and make better decisions. Digital Twin allows us to operate, maintain, or repair systems when we aren’t within physical proximity to them.

This session includes but not limited to: complex equipment modeling, complex equipment simulation, fault injection simulation, digital twin algorithm, PHM based on digital twin for aircraft, PHM based on digital twin for train, PHM based on digital twin for any other complex equipment etc.

● Guigang Zhang, Institute of Automation, Chinese Academy of Sciences, China. Email: guigang.zhang@ia.ac.cn.

● Tengfei Zhang, Institute of Automation, Chinese Academy of Sciences, China. Email: tengfei.zhang@ia.ac.cn.

● Guanghao Ren, Institute of Automation, Chinese Academy of Sciences, China. Email: guanghao.ren@ia.ac.cn.

● Yun Wang, Institute of Automation, Chinese Academy of Sciences, China. Email: y.wang@ia.ac.cn.

● Hua Ming, Institute of Automation, Chinese Academy of Sciences, China. Email: hua.ming@ia.ac.cn.

● Nan Yang, Institute of Automation, Chinese Academy of Sciences, China. Email: nan.yang@ia.ac.cn.

#6 Digital Twin for PHM

Digital Twin (DT) is being expected to help solve the technical challenges of engineering systems prognostics and health management (PHM). It uses digital forms to establish a virtual model of the object entity and utilizes data simulation to reproduce the operating state of the physical entity in the actual environment. Through data fusion, it expands new capabilities throughout the life cycle of physical entities, such as virtual-reality interaction and optimal decision-making. Focusing on the synchronization mapping of a physical system and virtual model, the DT-based PHM relies on DT data for quickly locating faults and verifying maintenance strategies. This special session is interested in collecting articles on the latest research progress and achievements of DT for PHM. Potential topics include but not limited to:

● Multiscale and multi-physical field modeling and fault simulation

● DT data interactive and dynamic update

● Order reduction of DT model

● DT-PHM methodology and decision making

● DT reliability design & dynamic evaluation

● DT-PHM engineering cases, system design and visualization

Prof. Gang Niu, Tongji University, China, Email:gniu@tongji.edu.cn

Dr. Yuejian Chen, Tongji University, China, Email:yuejianchen@tongji.edu.cn