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Attention to data pre-processing and data compression will reduce the instance number used for training, thus improving the accuracy and lowering the training complexity. In another study, a complex feature (called RW-TFCCD) consisting of five software features and one hardware feature was proposed for automatic fault diagnosis of aircraft batteries. It was noted that the features were extracted from the wavelet coefficients and their energy and local correlations with the fault type. Ankit et al. [74] applied the DL method (called RFAR) to automatically identify and classify rolling bearings faults. The trained model to the training set was used with then, it was trained with another data set and tested with another test set. Tuning of the model parameters was achieved by a grid search approach. The combination of RFAR with AI-based feature selection technique (called SPF) showed a superior fault diagnosis performance when compared with RFAR in isolation. FAUST and FAUST-TDA were applied in the fault identification and fault diagnosis of aircraft batteries. These two approaches have capabilities for not only the identification of batteries defective but also the differentiation of the fault type by applying the trained models with the training set. The data which was used in the study was simulated dataset and the efficiency of both methods was tested separately by training and testing the trained models with real data. Both algorithms achieved an accuracy higher than 90% for identifying the batteries’ defective condition by means of all the four fault types.

The data acquired from sensors will be normalized if required. The conventional conventional method is to normalize the original features by using average value or variance [12]. Sliced mean normalization is used for the time domain features, and the values of the features decrease gradually with the increasing frequencies. Mean, standard deviation and skewness were used for the frequency domain features. Hong et al. [56] used the Euclidean distance and the tolerance factor in the PCA space for feature selection, and also compared the performance of two feature extraction methods that are common to that adopted by Rao et al. [41]. The tests were conducted for the TEM valve using a combination of the above feature extraction methods. The best performing features were selected automatically. The proposed approach was compared with the expert system for the prevention and detection of health diseases using five multiclass diagnostic criteria. d2c66b5586