A review on signal processing techniques for bearing diagnostics

Abstract

Bearing is the most widely used component in many applications such as home appliances, industrial applications and military applications. Bearing works continuously in harsh environment especially for industrial use where the environment factor may affect the bearing conditions. Conditions of bearing require a proper monitoring to prevent sudden failure which will cause financial loss and threaten human life. Thus, a proper maintenance is introduced called Condition-based Maintenance (CBM). CBM is a maintenance strategy that provide a guideline to monitor the asset condition based on the information collected. In CBM, there are several important steps for monitoring the asset condition which one of them is signal processing. Signal Processing is important due to data acquire from the sensor is heavily masked by the background noise (machine sound and environment sound). Therefore, a robust signal processing technique is required to eliminate the noise and provide good features for decision making. This paper tends to review on the signal processing utilised for bearing fault diagnosis from the previous researcher.