Professor Hongpeng Yin is based at the School of Automation, Chongqing University in China. His current research interests mainly include data-driven process monitoring and fault diagnosis, pattern recognition, and data miningLi Cai received the B.E. degree from the School of Physics and Electronic Engineering from Hainan Normal University in 2019. He is currently undertaking a Ph.D. degree at the School of Automation, Chongqing University, China. His major research interests include data-driven fault detection and diagnosis, fault prediction, remaining useful life prediction, and (generalized) zero-shot learning Peng Zhang received the B.E. degree from College of Automation, Hangzhou Dianzi University, China in 2021. He is currently working towards a Ph.D. degree in the College of Automation, Chongqing University, China. His research interests include data mining, fault diagnosis and machine learning

• 1. Introduction2. Basic theories and methods of intelligent fault diagnosis and health prediction3. Multi-attribute learning framework for zero-sample fault detection in machinery4. Generalized zero-sample industrial fault diagnosis with domain bias5. Generalized zero-sample industrial fault diagnosis under cross-domain scenarios6. Learning across multisource domains for generalized zero-sample industrial fault diagnosis7. Federated generalized zero-sample industrial fault diagnosis across multisource domains8. A multi-phase Wiener process-based degradation model with imperfect maintenance activities

Professor Hongpeng Yin is based at the School of Automation, Chongqing University in China. His current research interests mainly include data-driven process monitoring and fault diagnosis, pattern recognition, and data miningLi Cai received the B.E. degree from the School of Physics and Electronic Engineering from Hainan Normal University in 2019. He is currently undertaking a Ph.D. degree at the School of Automation, Chongqing University, China. His major research interests include data-driven fault detection and diagnosis, fault prediction, remaining useful life prediction, and (generalized) zero-shot learning Peng Zhang received the B.E. degree from College of Automation, Hangzhou Dianzi University, China in 2021. He is currently working towards a Ph.D. degree in the College of Automation, Chongqing University, China. His research interests include data mining, fault diagnosis and machine learning

• 1. Introduction2. Basic theories and methods of intelligent fault diagnosis and health prediction3. Multi-attribute learning framework for zero-sample fault detection in machinery4. Generalized zero-sample industrial fault diagnosis with domain bias5. Generalized zero-sample industrial fault diagnosis under cross-domain scenarios6. Learning across multisource domains for generalized zero-sample industrial fault diagnosis7. Federated generalized zero-sample industrial fault diagnosis across multisource domains8. A multi-phase Wiener process-based degradation model with imperfect maintenance activities

Professor Hongpeng Yin is based at the School of Automation, Chongqing University in China. His current research interests mainly include data-driven process monitoring and fault diagnosis, pattern recognition, and data miningLi Cai received the B.E. degree from the School of Physics and Electronic Engineering from Hainan Normal University in 2019. He is currently undertaking a Ph.D. degree at the School of Automation, Chongqing University, China. His major research interests include data-driven fault detection and diagnosis, fault prediction, remaining useful life prediction, and (generalized) zero-shot learning Peng Zhang received the B.E. degree from College of Automation, Hangzhou Dianzi University, China in 2021. He is currently working towards a Ph.D. degree in the College of Automation, Chongqing University, China. His research interests include data mining, fault diagnosis and machine learning

• 1. Introduction2. Basic theories and methods of intelligent fault diagnosis and health prediction3. Multi-attribute learning framework for zero-sample fault detection in machinery4. Generalized zero-sample industrial fault diagnosis with domain bias5. Generalized zero-sample industrial fault diagnosis under cross-domain scenarios6. Learning across multisource domains for generalized zero-sample industrial fault diagnosis7. Federated generalized zero-sample industrial fault diagnosis across multisource domains8. A multi-phase Wiener process-based degradation model with imperfect maintenance activities