Deep optimal feature extraction and selection-based motor fault diagnosis using vibration

The rolling elements of the induction motor are highly susceptible to faults. The detection and diagnosis of rolling element faults are accurate and reliable only when the extracted features are accurate. The paper proposes an approach for bearing and rotor fault diagnosis using deep optimal feature extraction and selection based on vibration signal analysis. The deep feature extraction is done using an ensemble deep models features extraction approach in which features are extracted from seven pretrained models are fused serially using serial-based feature fusion technique. This leads to a solution for a higher efficacy model, but at the cost of high processing time as the feature data set gets large. A unique approach termed Ensemble Feature Selection has been developed to address this issue and limit the harmful impact of unwanted features in data-driven diagnostics. The processing time is further reduced using the shallow classifier at the fully connected layer. The proposed model is tested using the data acquired in the laboratory and validated using the available online benchmark data sets.