Rotating machine fault detection using principal component analysis of vibration signal

Abstract

Current vibration based maintenance methods can be improved by using principle component analysis to identify fault patterns in rotating machinery. The intent of this paper is to study the effects of using principle component analysis in a vibration based fault detection process and to understand the capability of this method of maintenance. Because vibration-based maintenance practices are capable of identifying motor faults based on their respective vibration patterns, principle component analysis observed in frequency domain can be used to automate the fault detection process. To test this theory, an experiment was set up to compare health conditions of a motor and determine if their patterns could be grouped using principle component analysis. The result from this study demonstrated that the proposed method successfully identified healthy, unbalance and parallel misalignments of rotary rotor. Therefore, it is capable of detecting faults in early stages and reducing maintenance costs.