Corrosion Fault Diagnosis of Rolling Element Bearing under Constant and Variable Load and Speed Conditions

Rolling element bearing defects is one of the main reasons for breakdown in electrical machines. Robust fault analysis (FA) including the diagnosis of faults and predicting their level of fault severity is thus necessary to optimise maintenance, improve reliability and to avoid more catastrophic failure consequences. The proposed diagnostic methods in this paper use the innovative discrete wavelet transform (DWT) for feature extraction and an orthogonal fuzzy neighbourhood discriminative analysis (OFNDA) approach for feature reduction. The dynamic recurrent neural network predicts the conditions of components and classifies faults under different operating conditions. The results obtained from the real time simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and accurately.