A New Bearing Fault Diagnosis Method using Envelope based Feature Extraction

The performance of conventional intelligent diagnostic methods depends on the feature extraction of faulty signals. Feature extraction from defective signals requires signal processing techniques, expert knowledge, and human exertion. In this study, a time series based classification technique was developed to detect malfunctions occurring in induction motors. The proposed algorithm only takes the vibration signals as input. The phase space of the vibration signals taken as input is generated according to the time delay and embedding dimension for each motor status. Each motor condition is divided into two classes: faulty and intact. After the envelope transform is enforced into each signal, phase space is obtained. Then the Gaussian mixture model is formed. After with K-means clustering algorithm, the classification has been made up of four classes which are normal, outer race, inner race, and ball. The proposed approach has been tested on normal and defective vibration signals and results with high accuracy have been obtained.