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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.

In industrial applications, the uptime of machines can be enhanced through equipment monitoring. This minimizes the risks of unpredicted failures and consequent plant outages. Since all failure modes can cause an increase in machine vibrations, monitoring this area is the predominant and most widely used method to determine equipment condition, and to predict failures. The objective of this study is to detect faults in rotating equipment with the use of vibration analysis. A motor condition monitoring experiment is set up, and the motor's operational speed is controlled by an AC motor drive. The vibration of the motor is measured and monitored. The measured vibration data is analyzed using spectrum analysis software and a MATLAB program. The overall vibration level is monitored, the vibration severity is compared with the standard severity table and is used to determine the condition of the motor. The specific natural frequency corresponds with which kind of fault or failure mode is identified. This information provide insight on the condition of the machine.