## 1 Introduction
Condition data for IMs can be collected in different forms as acoustic emission [8], vibration, temperature readings, current sources, and thermal images, which are extensively investigated for bearing fault investigation [9]. Recently, vibration signal analysis for fault analysis has gained much attention for monitoring health of the machine due to the capacity to transfer intrinsic information of mechanical systems [10; 11]. External vibration sensors (e.g., accelerometers) are required to be mounted around the bearing, which are very costly, and the installation process requires full time access to the IM. Because of these drawbacks, the bearing based on vibration-signal is not easily accessible in remote locations or for a less complex system. On the other hand, using current signal for condition monitoring offers some advantages over the vibration signal as it does not require external sensors for data collection. Moreover, if there are no frequency inverters or current transformers, the stator current from a single input source can be measured with the help of current transducers [12]. Hence, due to the cost-effective and noninvasive nature, motor current signal analysis (MCSA) is considered one of the most proficient condition monitoring techniques. Also, multiple signature analysis combining motor current and vibration signal analyses has been performed to ensure higher reliability [13]. From the raw data fault, classification or identification is not possible. With various signal processing techniques, the sensor data are analyzed by extracting fault information. Some widely used approaches for signal analysis are time-domain analysis [14], frequency-domain analysis [15], enhanced frequency analysis [16], and time-frequency analysis techniques [17]. The techniques based on signal processing do not need any equivalent circuit model, and the performance of the system depends on the working condition of the machine. Therefore, the computational cost may increase if complexity of the advanced signal processing tool is increased. The root mean square, short impulse method, and high-order statistics are some of the frequently used time-domain techniques. In frequency-domain analysis, Fast Fourier transform (FFT), envelope analysis, and high-order spectral analysis [18] are generally used. FFT is the most widely used method due to less computational complication in comparison with the other methods. For any signal with non-stationary nature, which is common, frequency domain analysis is not widely applicable because it is not able to reveal the intrinsic information. Generally, the running machine contains many non-stationary components because of environmental change and faults from the machine itself. Therefore, it is significant to evaluate the signals with non-stationary type, with the help of various time-frequency analyses, such as short-time Fourier transform (STFT), the Wigner-Ville distribution (WVD), and wavelet transform. Application of this technique revealed both the time and frequency domain information necessary for investigation. In machine learning-based fault analysis, signal processing is generally used for signal conditioning and feature extraction and include cyclic spectral analysis [19], statistical analysis, [20] wavelet analysis [21; 22; 23], Hillbert-Hung transform [24], and correlation [25]. Therefore, because of the exclusive characteristics of wavelet analysis, it is used widely for analyzing non-stationary signals. It is used for fault diagnosis in gears and bearings [26; 27], locating fault and crack size determination in different structures and components [28; 29]. In detection and extraction of features for fault classification, many researchers reported successful implementation of wavelet transform [30; 31; 32]. Various types of faults in the power system [33] are successfully differentiated from three-phased voltage signal decomposition up to only 4 th level using DWT.