A unified approach to detecting and trending changes caused by mechanical faults in rotating machinery

For detecting faults in rotating machinery, different fault types generally require different techniques for the effective detection of the faults; hence, many different techniques have been developed. However, we often find that the existing detection techniques are either incapable or ineffective for many new fault types. Therefore, we will need to develop brand new methods after the fault event. This can significantly constrain the usefulness and effectiveness of machine health monitoring systems. In this article, we propose a unified signal processing approach to detecting and trending changes caused by various types of faults in rotating machinery. The theoretical foundation of the proposed technique is based on extracting the differences between the synchronously averaged (angularly resampled) signals acquired under the changing health conditions after aligning their phases. The aligning of the phases is achieved through a novel and efficient approach, which is performed in the frequency domain. The alignment enables a direct comparison between the amplitudes of the synchronously averaged signals and provides a trustworthy evaluation using the same reference datum. The proposed method is novel and conceptually unsophisticated, and its effectiveness is demonstrated using vibration data from rotating 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 faults in rotating machines. This is an ideal technique to be implemented for testing on aircraft engine onboard health monitoring systems.