Detection of common motor bearing faults using frequency-domain vibration signals and a neural network based approach

Bearings and their vibration play an important role in the performance of all motor systems. In many cases, the accuracy of the instruments and devices used to monitor and control the motor system is highly dependent on the dynamic performance of the motor bearings. In addition, many problems arising in motor operation are linked to bearing faults. Thus, fault detection of a motor system is inseparably related to the diagnosis of the bearing assembly. The paper presents an approach using neural networks to detect common bearing defects from motor vibration data. The results show that neural networks can be an effective agent in the detection of various motor bearing faults through the measurement and interpretation of motor bearing vibration signals.