Fault detection in a ball bearing system using minimum variance cepstrum

Abstract

The signals that can be obtained from rotating machines convey the information of machine. For example, if the machine under investigation has faults, it generates the signal that is composed of pulse signals. This paper addresses the way in which we can find the faults of which the source generates periodic pulse signal. Specifically, we have interest when it is embedded in noise. How well we can detect the fault signal in noise directly determine the quality of a fault diagnosis of rotating machines. We propose a signal processing method to detect fault signals in noisy environments. The proposed method is suggested to be called as ‘minimum variance cepstrum’, because it minimizes the variance of the signal power at the cepstrum domain. To test the performance of this technique, various experiments have been performed for ball bearing elements that have man made faults. Results show that the proposed technique is quite powerful in the detection of fault in noisy environments. In other words, it is possible to detect faults earlier than before by using this technique.