Abstract
Rolling element bearings are widely used components in industrial equipments and their failure may result in severe damage of critical processes. Bearing plants are required to provide estimated performance data to their customers. These statistically based data are usually got through various testing experiments with product samples via bearing test rig. However, it is usually hard to get convincible results from traditional method due to the limitation of monitoring device and analysis methods. This paper designs an intelligent system for a bearing testing and inspection center of a bearing plant to monitor the health status and assess the performance of bearings being tested. The system has a distributed infrastructure and can continually collect testing data, analyze vibration signals, extract features of bearing fault, diagnose bearing faults and further assess the quality of the bearings. Fuzzy logic, wavelet neural network and dynamic wavelet neural network are employed as diagnostic/prognostic algorithm