Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 503
Special Issue Editors
Interests: fault diagnosis; prognosis; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: fault detection and diagnosis; signal processing; multiscale signal analysis; sensor fusion; signal to image conversion and analysis; artificial intelligence; explainable machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Manuscript Submission Information
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Keywords
- bearing fault diagnosis
- single sensor techniques
- sensor fusion
- condition monitoring
- machine learning
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