As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring an...As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring and diagnostics methodologies,categorizing them into in-context learning,fine-tuning,retrievalaugmented generation,multimodal learning,and time series approaches,analyzing advances in diagnostics and decision support.It identifies bottlenecks like limited industrial data and edge deployment issues,proposing a three-stage roadmap to highlight LLMs’potential in shaping adaptive,interpretable PHM frameworks.展开更多
Power grids deliver energy,and telecommunication networks transmit information.These two facilities are critical to human society.In this study,we conduct a comprehensive overview of the development of reliability met...Power grids deliver energy,and telecommunication networks transmit information.These two facilities are critical to human society.In this study,we conduct a comprehensive overview of the development of reliability metrics for power grids and telecommunication networks.The main purpose of this review is to promote and support the formulation of communication network reliability metrics with reference to the development of power grid reliability.We classify the metrics of power grid into the reliability of power distribution and generation/transmis-sion and the metrics of telecommunication network into connectivity-based,performance-based,and state-based metrics.Then,we exhibit and discuss the difference between the situations of the reliability metrics of the two systems.To conclude this study,we conceive a few topics for future research and development for telecommunication network reliability metrics.展开更多
High-speed trains(HSTs)have the advantages of comfort,efficiency,and convenience and have gradually become the mainstream means of transportation.As the operating scale of HSTs continues to increase,ensuring their saf...High-speed trains(HSTs)have the advantages of comfort,efficiency,and convenience and have gradually become the mainstream means of transportation.As the operating scale of HSTs continues to increase,ensuring their safety and reliability has become more imperative.As the core component of HST,the reliability of the traction system has a substantially influence on the train.During the long-term operation of HSTs,the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures,thus threatening the running safety of the train.Therefore,performing fault monitoring and diagnosis on the traction system of the HST is necessary.In recent years,machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis.Machine learning has made considerably advancements in traction system fault diagnosis;however,a comprehensive systematic review is still lacking in this field.This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint.First,the structure and function of the HST traction system are briefly introduced.Then,the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed.Finally,the challenges for accurate fault diagnosis under actual operating conditions are revealed,and the future research trends of machine learning in traction systems are discussed.展开更多
文摘As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring and diagnostics methodologies,categorizing them into in-context learning,fine-tuning,retrievalaugmented generation,multimodal learning,and time series approaches,analyzing advances in diagnostics and decision support.It identifies bottlenecks like limited industrial data and edge deployment issues,proposing a three-stage roadmap to highlight LLMs’potential in shaping adaptive,interpretable PHM frameworks.
文摘Power grids deliver energy,and telecommunication networks transmit information.These two facilities are critical to human society.In this study,we conduct a comprehensive overview of the development of reliability metrics for power grids and telecommunication networks.The main purpose of this review is to promote and support the formulation of communication network reliability metrics with reference to the development of power grid reliability.We classify the metrics of power grid into the reliability of power distribution and generation/transmis-sion and the metrics of telecommunication network into connectivity-based,performance-based,and state-based metrics.Then,we exhibit and discuss the difference between the situations of the reliability metrics of the two systems.To conclude this study,we conceive a few topics for future research and development for telecommunication network reliability metrics.
基金supported by the National Natural Science Foundation of China(Grant No.71731008)the Beijing Municipal Natural Science Foundation-Rail Transit Joint Research Program(Grant No.L191022)the Zhibo Lucchini Railway Equipment Co.,Ltd.
文摘High-speed trains(HSTs)have the advantages of comfort,efficiency,and convenience and have gradually become the mainstream means of transportation.As the operating scale of HSTs continues to increase,ensuring their safety and reliability has become more imperative.As the core component of HST,the reliability of the traction system has a substantially influence on the train.During the long-term operation of HSTs,the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures,thus threatening the running safety of the train.Therefore,performing fault monitoring and diagnosis on the traction system of the HST is necessary.In recent years,machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis.Machine learning has made considerably advancements in traction system fault diagnosis;however,a comprehensive systematic review is still lacking in this field.This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint.First,the structure and function of the HST traction system are briefly introduced.Then,the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed.Finally,the challenges for accurate fault diagnosis under actual operating conditions are revealed,and the future research trends of machine learning in traction systems are discussed.
