A Generalized Machine Fault Detection Method Using Unified Change Detection

Abstract


 
 
 Many different techniques have been developed for detecting faults in rotating machinery. This is because different fault types typically require different techniques for the effective detection of the fault. However, for many new or unknown fault types, we have found that the existing detection techniques are either incapable or ineffective and that we therefore need to come up with brand new methods after the fault event. This can significantly constrain the usefulness and effectiveness of Prognostic Health Management (PHM) systems. In this paper we attempt to look at detecting global changes in the synchronously averaged signals as the machine’s health status progresses from healthy to faulty, and to define one unified signal processing technique and its associated condition indicators for the detection of changes caused by various types of faults in rotating machinery. The proposed method is conceptually very simple, and its effectiveness is demonstrated using vibration data from machines with several different types of faults. The results have shown that this single unified change detection approach can be very effective in detecting and trending changes caused by many different types of machine faults.