Advances in Condition Monitoring and Fault Diagnosis

Dear Colleagues,

In recent years, significant progress has been made in advanced technologies for rotating machinery in the fields of condition monitoring (CM) and fault detection and diagnosis (FDD). These advances are mainly driven by the growing demand in the industrial sector for improved reliability, efficiency, and safety. In particular, CM for expensive and high-value machinery is crucial for tracking work status and performance, reducing maintenance costs, improving efficiency and reliability, and the early warning of mechanical failures. The CM and FDD for rotating machinery mainly focus on signal processing and diagnostic techniques, including time, frequency, and time–frequency domains, as well as anomaly detection, image processing, data fusion, data mining, time series analysis, and expert systems. In addition, there are some studies focusing on specific fields, such as wind energy, photovoltaic power generation, and drive motors and batteries in electric vehicles. These studies involve various technologies and methods, and expand the exploration of the application of CM and FDD.

This Special Issue welcomes theoretical and practical contributions aimed at further understanding intelligent techniques, including anomaly detection, image processing, data fusion, data mining, time series analysis, and expert systems. Moreover, it seeks reports on innovative machines and power systems with applications of CM and FDD. This Special Issue welcomes both original research articles and review articles. Potential topics include, but are not limited to, the following:

• Advanced data observation and acquisition techniques to carry out rotating machinery condition monitoring;
• Advanced digital signal processing methodologies for big data to solve the Prognostic and Health Management (PHM) problem of power equipment;
• Evolution and detection technology of key mechanical component defects in extreme environments;
• Data repair, verification methods, and anomaly monitoring methods for incomplete observation samples;
• Advanced fault-informative feature (e.g., on the time domain and time–frequency domain) representative methods for local defect detection;
• Spectrum-based capability evaluation on noise disturbance robustness, and weak diagnostic signal enhancement;
• Denoising methods and real state signal separation techniques under strong background noise;
• Exploration of hybrid expert model applications based on large language model architectures.

We look forward to receiving your contributions.

Dr. Yunfeng Yan
Dr. Xian-Bo Wang
Guest Editors

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• condition monitoring
• fault detection and diagnosis
• reliability analytics of power systems