Bearing is one of the most crucial parts in induction motor (IM) as a result there is a constant call for effective diagnosis of bearing faults for reliable operation. Infrared thermography (IRT) is appreciably used as a non-destructive and non-contact method to detect the bearing defects in a rotary machine. However, its performance is limited by insignificant information and string noise present in the infrared thermal image. To address this issue, an emergent two dimensional discrete wavelet transform (2D-DWT) based IRT method has been proposed in this paper for diagnosing the different bearing faults in IM, namely, inner and outer race defects, and lack of lubrication. The dimensionality of the extracted features was reduced using principal component analysis (PCA) and thereafter the selected features were ranked in the order of most relevant features using the Mahalanobis distance (MD) method to achieve the optimal feature set. Finally these selected features have been passed to the complex decision tree (CDT), linear discriminant analysis (LDA) and support vector machine (SVM) for fault classification and performance evaluation. The classification results reveal that the SVM outperformed CDT and LDA. The proposed strategy can be used for self-adaptive recognition of bearing faults in IM which helps to avoid the unplanned and unwanted system shutdowns due to the bearing failure.

Electric Vehicle (EV) is crucial for future transportation which will improve fuel economy and contributes toward reduction of emissions. EVs are becoming increasingly integrated component of transportation in order to fulfil ever-increasing demands for improved performance with safety and reduced environmental impact. Therefore, to increase the efficiency and reduce the maintenance cost of these vehicles, an early fault diagnosis system is essential. This enables early detection of deterioration of the vehicle’s health, alleviating a proactive solution and reducing the sudden component breakdown. Keeping the need for further research in mind, this paper provides a comprehensive review of the state-of-the-art Condition Monitoring (CM) and Fault Diagnosis (FD) strategies for various component of electric vehicles. The challenges and suggestions for future work on emerging technology in an electric vehicle are also addressed to promote further targeted research activities based on the current needs.