## 1 Introduction
Although many variations of wavelet functions exist, it is crucial to choose a suitable wavelet to find out the best match and extract the most suitable features. After the initial signals are transformed into a compact relevant representation, they are act as input of a classifier to train and improve the decision function. Among the various processes of classification, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are mostly implemented for machine fault detection and identification [34; 35; 36; 37]. Now a days, extreme learning machine (ELM), a neural network having single hidden-layer-feed-forward has been implemented [38] for fault detection and classification and has a very fast learning rate and higher accuracy for prediction in comparison with SVM and ANN. Recently, deep learning (DL) approaches have been considered for fault diagnosis. The DL technique consists of multiple levels of non-linear operation and can automatically learn up to high-level features to allow decision-making more intelligently. DL methods, such as the Deep Belief Networks (DBN), Stacked Auto Encoders (SAE), and Convolutional Neural Network (CNN), have been investigated recently in fault diagnosis [39; 40; 41; 42]. Despite attaining an effective solution from ML techniques, these methods often become stacked in a local minimum, if the configuration parameters are not efficiently considered [43]. In recent times, researchers have started to apply a modernized approach known as ensemble learning to avoid the drawbacks of ML approaches such as feature selection, incremental learning, class-imbalance data, as well as learning concept drift from non-stationary distributions [44]. Ensemble learning is one example of the ML prototypes where multiple learners need to be trained for solving a single problem. Moreover, this method provides better generalization to adapt to any unknown case, better efficiency of avoidance from local minima, and greater search abilities than any ordinary ML approach. Random forests and extreme gradient boosting (XGBoost) are two well-known ensemble ML methods proposed by [PERSON] [45] in 2001 and Dr. [PERSON] [46] in 2014, respectively. The XGBoost algorithm possesses low computational complexity, high accuracy, and fast running speed for any input data set size due to utilization of the central processing unit (CPU) with multi-threaded parallel computing. These methods are effectively used in various fields, such as prediction of environmental condition, detection of medical symptoms, and diagnosis of machine faults [47; 48; 49]. In this study, we used three types of discrete wavelet transform (DWT) for signal decomposition. Later, statistical features were calculated from the high-level approximation coefficients and the detail coefficients to reduce the feature matrix dimension. After that, to estimate the performances, two ensemble learning algorithms, RF and XGBoost, were trained to classify. Finally, a comparison is made with some recent works on motor current signal, where [PERSON] et al. [50] applied particle swarm optimization based support vector machine (SVM-PSO) and in [51; 52], the authors used deep learning based approaches for IM fault classification. Therefore, the main contributions of this paper can be listed as follows:  
* Evaluate the performances of various wavelets for motor current signal analysis. * Observe the effect of denoising the current signal on classification accuracy. * Evaluate the performance of two ensemble classifiers for bearing fault identification of an IM. The remainder of this paper is organized as follows. Section 2 provides a description of the experiment set up and characteristics of the data. Section 3 describes the overall process of the method utilized in this paper. Section 4 explains the experimental results and performance of the proposed approach using the evaluation parameters from the dataset, and Section 5 concludes the paper.