Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach

Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guide the moving parts of the machine and reduce friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the bearing health condition monitoring. The defects found in the bearings can lead to malfunctioning of the machinery and impact the level of production. This research presents a detailed detection technique and diagnosis of bearing defects using a novel hybrid approach (continuous wavelet transform, Abbott-Firestone parameter, and artificial neural network). The vibration signals were obtained from Case Western Reserve University. An algorithm is developed for abnormal condition detection and diagnostics using intelligent systems. MATLAB is used to analyse the vibration signals, test, and train the required models according to the chosen model structure. Various statistical features are extracted from the time domain namely kurtosis, skewness, root mean square, standard deviation, crest factor and Abbott parameters to analyse and identify the bearing fault. The results demonstrate that the proposed method is effective in identifying the bearing faults. The outcome from this project would lead to development of an easy to use tool for bearing fault detection and diagnostics.