Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches

Dear Colleagues,

Bearings are fundamental components in rotating machinery, supporting motion and reducing friction between moving parts. They are widely used in industries such as manufacturing, transportation, and aerospace. As machinery becomes more advanced and operates under increasingly demanding and harsh conditions, bearings are subjected to high loads, speeds, and prolonged operation. These factors contribute to wear and the development of incipient faults, which, if undetected, can lead to severe consequences, including machine failure, downtime, economic loss, and safety hazards. Therefore, accurate and early diagnosis of bearing faults is essential for improving equipment reliability, optimizing maintenance, and ensuring operational safety.

This Special Issue focuses on recent advances in bearing fault diagnosis using both single-sensor techniques and multi-sensor fusion approaches. Single sensor systems are favored for their simplicity and low cost, while sensor fusion strategies enhance fault detection and classification by integrating data from multiple sensing modalities such as vibration, acoustic emission, and current signals, etc.

This Special Issue invites original research articles and reviews that explore novel methodologies, signal processing techniques, machine learning models, deep learning architectures, and practical applications related to both single-sensor and sensor fusion-based bearing fault diagnosis. Furthermore, research concerning recent advances in sensor fault diagnosis is also encouraged.

Prof. Dr. Jong-Myon Kim
Dr. Zahoor Ahmad
Guest Editors

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