Robust Incipient Fault Detection of Complex Systems Using Data Fusion
Yupeng Wei, Dazhong Wu, Janis Terpenny
Abstract
An incipient fault refers to the first change point when a system starts to deteriorate. Early detections of incipient faults are crucial to the safety, reliability, and effective predictive maintenance of complex engineering systems. However, it is very difficult to detect incipient faults at the initial stage of the system-degradation processes. To address this issue, an innovative data-fusion method is introduced to detect the incipient faults by integrating data collected from multiple sources instead of a single data source. The data-fusion problem is formulated as a convex optimization problem, aiming at reducing the false-alarm rate and the time to detect incipient faults. We demonstrate the proposed data-fusion method using a data set generated by the degraded aircraft engines. The numerical results have demonstrated that the proposed method can assist in reducing both the false-alarm rate and the time to detect incipient faults.