Research Topic

Rotating Machinery Condition Monitoring under Non-Stationary Operations

About this Research Topic

Condition monitoring techniques have been developed and widely used to monitor the health of rotating machines and components, such as motors, gearboxes, wind turbines, helicopters, mining equipment, and engines. Many signal processing and machine learning methods have been developed for rotating machinery under stationary operations. In practice, rotating machines often operate under non-stationary conditions due to fast varying loads and rotational speeds. Existing methods for signal processing under stationary conditions are not suitable for analyzing non-stationary vibration signals. Therefore, it is necessary to develop methods of signal analysis for rotating machinery condition monitoring under non-stationary operations.

This Research Topic aims to provide a platform to present high-quality original research, as well as review articles, on the latest developments in condition monitoring techniques for rotating machinery under non-stationary operations. Potential topics include but are not limited to:

• Development of signal processing methods with relation to non-stationary machine operation
• Sensor and data collection, data quality management, data processing, data fusion technologies for PHM
• Data-driven methods for fault detection, diagnosis and prognosis of a machine under non-stationary operating conditions
• Hybrid methods for fault diagnostics and prognostics
• Faults separation in the machine degradation process using signal analysis
• Signal interferences identification in complex machinery systems


Keywords: Rotating Machines, Condition Monitoring, Non-Stationary Operations, Fault Diagnosis, Feature Extraction


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.

Condition monitoring techniques have been developed and widely used to monitor the health of rotating machines and components, such as motors, gearboxes, wind turbines, helicopters, mining equipment, and engines. Many signal processing and machine learning methods have been developed for rotating machinery under stationary operations. In practice, rotating machines often operate under non-stationary conditions due to fast varying loads and rotational speeds. Existing methods for signal processing under stationary conditions are not suitable for analyzing non-stationary vibration signals. Therefore, it is necessary to develop methods of signal analysis for rotating machinery condition monitoring under non-stationary operations.

This Research Topic aims to provide a platform to present high-quality original research, as well as review articles, on the latest developments in condition monitoring techniques for rotating machinery under non-stationary operations. Potential topics include but are not limited to:

• Development of signal processing methods with relation to non-stationary machine operation
• Sensor and data collection, data quality management, data processing, data fusion technologies for PHM
• Data-driven methods for fault detection, diagnosis and prognosis of a machine under non-stationary operating conditions
• Hybrid methods for fault diagnostics and prognostics
• Faults separation in the machine degradation process using signal analysis
• Signal interferences identification in complex machinery systems


Keywords: Rotating Machines, Condition Monitoring, Non-Stationary Operations, Fault Diagnosis, Feature Extraction


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.

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Submission Deadlines

15 August 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

15 August 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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