Recently, advanced signal processing methods for fault diagnosis have seen a growing amount of interest. Despite this, more powerful and applicable signal processing methods are still required to make fault diagnosis more reliable and practical in industrial applications. Credible and reliable fault diagnosis technologies are an urgent need for many manufacturing companies. Recent advances in signal processing have made highly effective condition monitoring and prognosis available for key equipment, such as aircraft engines, wind turbines, high-speed trains, CNC machines, etc.
This Research Topic therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of mechanical signal processing.
Potential topics include but are not limited to:
• Sensing technologies for condition monitoring and fault diagnosis;
• Interpretable condition monitoring and fault diagnosis based on machine learning or deep learning;
• Multisensory signal fusion methods;
• Signal enhancement methods;
• Data analytics and condition monitoring via ML/AI techniques;
• Dynamic models and model-based techniques;
• Physical-informed signal processing methods or AI techniques;
• Time-frequency analysis methods;
• Angular approaches;
• Predictive maintenance using artificial intelligence;
• Feature fusion methods;
• Health indicators for condition monitoring and fault diagnosis;
• Digital-twin-based fault diagnosis.
Keywords:
signal processing, condition monitoring, fault diagnosis
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.
Recently, advanced signal processing methods for fault diagnosis have seen a growing amount of interest. Despite this, more powerful and applicable signal processing methods are still required to make fault diagnosis more reliable and practical in industrial applications. Credible and reliable fault diagnosis technologies are an urgent need for many manufacturing companies. Recent advances in signal processing have made highly effective condition monitoring and prognosis available for key equipment, such as aircraft engines, wind turbines, high-speed trains, CNC machines, etc.
This Research Topic therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of mechanical signal processing.
Potential topics include but are not limited to:
• Sensing technologies for condition monitoring and fault diagnosis;
• Interpretable condition monitoring and fault diagnosis based on machine learning or deep learning;
• Multisensory signal fusion methods;
• Signal enhancement methods;
• Data analytics and condition monitoring via ML/AI techniques;
• Dynamic models and model-based techniques;
• Physical-informed signal processing methods or AI techniques;
• Time-frequency analysis methods;
• Angular approaches;
• Predictive maintenance using artificial intelligence;
• Feature fusion methods;
• Health indicators for condition monitoring and fault diagnosis;
• Digital-twin-based fault diagnosis.
Keywords:
signal processing, condition monitoring, fault diagnosis
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.