This research tackles a critical challenge in the condition monitoring of rotating machinery by introducing an advanced diagnostic approach that enhances early fault detection. Rotating machineries are essential in industries but are prone to issues like bearing defects, which can lead to costly downtimes, safety hazards, and operational inefficiencies. While Motor Current Signature Analysis (MCSA) has long been a researched technique for condition monitoring, traditional higher-order statistical methods, such as bicoherence (BC) and Cross-Correlation of Complex Spectral Components of Order 3 (CCCS3), face limitations in detecting subtle, complex fault interactions, particularly in non-stationary environments where conditions vary rapidly. This research presents a groundbreaking advancement through the Cross-Correlation of Spectral Moduli of Order 3 (CCSM3), a novel technique built upon MCSA that overcomes the limitations of conventional methods. CCSM3 enhances fault detection by focusing on the correlation of frequency component moduli, bypassing phase information that can be unpredictable in realworld scenarios. The unique aspect of CCSM3 lies in its ability to simultaneously analyse three frequency components, offering a more robust and sensitive approach to detecting faults in rotating machinery. Through Gaussian modelling and bilinear system simulations, the technique was validated under controlled conditions that mimicked various fault severities. Additionally, the study conducted extensive experimental trials on airport baggage conveyor system, demonstrating the real-world applicability and practical effectiveness of CCSM3. Achieving Technology Readiness Level (TRL) 7, these trials confirmed that CCSM3 can be implemented in operational environments with significant success. The research findings show the superiority of CCSM3 over traditional methods in terms of fault detection and diagnostic sensitivity. Metrics such as the Total Probability of Correct Diagnosis (TPCD) and Fisher Criterion (FC) were used to quantify performance, showcasing the CCSM3 technique’s ability to detect faults with greater precision. This advancement represents a major step forward in the integration of MCSA with higher-order spectral techniques, offering a non-intrusive, cost-effective solution for real-time monitoring and predictive maintenance. The proposed CCSM3 technique sets a new standard in the condition monitoring of rotating machinery. By addressing the shortcomings of conventional MCSA and higher-order spectral analysis methods, CCSM3 enables more, reliable, and detection of faults, promoting safer, more efficient, and longer-lasting industrial operations.
| Date of Award | 4 Jun 2026 |
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| Original language | English |
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| Supervisor | Len Gelman (Main Supervisor) & Andrew Ball (Co-Supervisor) |
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