About this Research Topic
Currently, benefit from the update of distributed sensor networks, Internet of Things (IOT) technology and computational power, the use of data-driven and network learning (DDNL) methods/techniques are prevailing to evaluate the performance, condition and reliability of industrial systems such as electromechanical device. However, the DDNL fails to provide any physical interpretation and connection for the degradation mechanism of systems.
In this research topic, to prolong the lifespan and enhance the performance and reliability of the system, statistical physical-informed knowledge and evolutional laws, including theoretical, analytical, and experimental investigations need to be focused and incorporated for bridging the gap between the invisible data phenomena and visible macro failure of the system, based on probability analysis and statistical distribution. The aim of this research topic is to collect the latest and original research and review articles that describe theoretical and experimental findings as well as some specific applications related to statistical physical-informed methods for condition monitoring of complex systems. The research, findings and ideas of this research topic should be physical-mechanisms and laws-oriented and intended to improve the state-of-the-art.
Potential topics of interest to this research topic include, but are not limited to, the following:
• Parametric and semi-parametric statistical model;
• Statistical model and probability model for mechanisms and laws finding of system degradation;
• Chaotic attractors, fractals, solitons and turbulence models for condition monitoring of complex systems;
• Statistical model regarding hydromechanics and electromagnetism for condition monitoring of complex systems;
• Degradation prognosis and performance evaluation based on nonlinear statistical and physical-informed models;
• Datasets mining and mechanism verifications based on statistical physical-informed enabler;
• Algorithms & models development of physics-informed and network learning;
In this research topic, to prolong the lifespan and enhance the performance and reliability of the system, statistical physical-informed knowledge and evolutional laws, including theoretical, analytical, and experimental investigations need to be focused and incorporated for bridging the gap between the invisible data phenomena and visible macro failure of the system, based on probability analysis and statistical distribution. The aim of this research topic is to collect the latest and original research and review articles that describe theoretical and experimental findings as well as some specific applications related to statistical physical-informed methods for condition monitoring of complex systems. The research, findings and ideas of this research topic should be physical-mechanisms and laws-oriented and intended to improve the state-of-the-art.
Potential topics of interest to this research topic include, but are not limited to, the following:
• Parametric and semi-parametric statistical model;
• Statistical model and probability model for mechanisms and laws finding of system degradation;
• Chaotic attractors, fractals, solitons and turbulence models for condition monitoring of complex systems;
• Statistical model regarding hydromechanics and electromagnetism for condition monitoring of complex systems;
• Degradation prognosis and performance evaluation based on nonlinear statistical and physical-informed models;
• Datasets mining and mechanism verifications based on statistical physical-informed enabler;
• Algorithms & models development of physics-informed and network learning;
Keywords: Statistical physical-informed models, Physical-mechanisms laws-oriented, Probability statistical distribution, Nonlinear physical models, Condition monitoring of complex systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
