Bearing Fault Detection using Vibration Analysis

Abstract

In the current scenario, condition monitoring is an essential technique for the maintenance of machinery. Various methods such as vibration analysis, temperature analysis, and wear and debris analysis have been introduced, with vibration analysis proving to be the most effective for detecting machine faults. This study focuses on bearing fault detection using vibration analysis. Bearing faults generate impact forces that alter vibration patterns. We examined the vibration patterns of a 0.25HP PMDC motor under different load conditions and at varying RPMs, comparing good and faulty bearings to detect inner and outer race bearing faults. Inner race defect detection is challenging under poor signal-to-noise ratio (SNR) conditions due to the vibration signal being masked by other vibrations. To enhance SNR, Principal Component Analysis (PCA) was utilized, and Artificial Neural Networks (ANN) algorithms were employed. Rotating machinery, widely used in industries such as petroleum, automotive, HVAC, and food processing, relies on bearings for rotational or linear movement, reducing friction and stress. Rolling Element Bearings (REBs) offer a favorable balance of friction, lifetime, stiffness, speed, and cost. Thus, real-time monitoring and diagnosis of bearings are critical to prevent failures, enhance safety, avoid unforeseen production downtime, and reduce costs. This study proposes an approach based on Wavelet Transform and ANN to analyze vibration signals from rolling element bearings and to identify and classify component defects.