Data-driven sensor selection for industrial gearbox fault diagnosis using principal component analysis

Abstract The failure of gearboxes, a critical component of mechanical power transmission systems, can significantly disrupt process cycle times and decrease production line throughput. Predicting failures in gear transmission systems is notably challenging due to their complex geometry and the interaction of simultaneous faults, which complicates fault isolation. Typically, multiple sensors are deployed at various locations to isolate and analyse faults in gearboxes. However, not all sensor data yield reliable results, making it crucial to select the most effective sensors. This study employs a Principal Component Analysis (PCA)-based approach to select the best sensor for fault detection. The results show an 18% increase in fault detection accuracy when using the most effective sensor. To validate the proposed approach, experiments were conducted under four gear conditions, considering different speeds, loads, fault severities, and fault types.