Fault diagnosis of electric motors using vibration signal analysis

In industrial applications, especially in manufacturing environments, electric motors are employed practically everywhere. They are necessary for many different sectors, which can sometimes make it challenging to prevent malfunctions and keep them operating at their best. Numerous defects can affect how well they work, but bearing-related errors are the most frequent reasons for motor failures. This research uses temporal and frequency domain analysis of vibration signals to identify motor faults. A public domain database has been used for the investigation and analysis. The findings show that electric motor problems, including inner raceway, outer raceway, and rolling element fault, can be identified and diagnosed using the time and frequency domain features extracted from the vibration signals. The effectiveness of the proposed technique is shown by comparing it with both the time domain and frequency domain techniques. The accuracy of the time domain and frequency domain techniques is 85.4% and 91.6% respectively. However, the proposed hybrid technique has a far better accuracy of 95.8% as compared to the two techniques.