## 1 Introduction  
As a basic segment of the present industrial plants, electrical machines expedite industrial tasks and productions. Owing to its robust, well-constructed, and simple design, induction motors (IMs) are widely used in manufacturing processes. Nevertheless, three-phase induction machines are subjected to inevitable electrical, mechanical, and environmental stresses. Among various failures occurred in different parts of a machine, broken rotor bar (BRB) is of significance due to its serious consequent malfunctions caused. Presence of broken rotor bar leads to torque reduction, incompatible motor operation and safety concerns [1]. In order to ensure the availability of industrial systems and the safety of goods and persons on the site, the monitoring and diagnosis of rotor faults are of prime importance. Rotor irregularity in the machine can be diagnosed at an early stage by the processingof the monitored signal. Employing an appropriate method of feature extraction that indicates the presence of rotor fault from the processed signal improves the classification of the motor condition and thus the performance of decision-making [2]. Comparing all numerous signals for feature extraction and fault diagnosis presented in [3], measuring the stator currents of an induction motor is more cost-effective and easy to implement for fault detection objectives. Motor current signature analysis was extensively used based on the monitoring of the sideband components at (1 \(\pm\) 2 \(k\)s)\(f_{s}\), where \(f_{s}\) is the fundamental frequency and s is the slip [4,5]. The intrinsic dynamic nature and/or the external excitations, especially under time-varying operational conditions during machine operations, often lead to non-stationary signals. Such non-stationary signals contain rich information about the machinery health conditions. Therefore, important fault features can be extracted from these signals for fault detection and diagnosis, if proper analysis methods are applied [6]. Feature extraction using fast Fourier transform (FFT) produces good results when the waveforms examined are stationary or periodic, but it is not suitable for non-stationary signals. In [7], the performance of Fourier and wavelet transform (WT) for efficient recognition of broken rotor bar in a squirrel-cage induction machine was evaluated. Wavelet transform is a time-scale representation of a signal, and its window width is automatically adjustable for various frequency components [8]. The way wavelet analysis localizes signal's information in the time-scale plane makes it more suitable for processing the non-stationary signals. Therefore, important fault features can be extracted from these signals for early identification of the faults through wavelet transform. Hence, in fault diagnostics domain, wavelet transform has been used to extract dominant frequency components and time variation features and describe a more precise behavior of the stator current signal [9]. In [10], novel indices for BRBs diagnosis is introduced for three-phase induction motors based on discrete wavelet coefficients of stator current in a specific frequency band. In [11], a review of using WT for rotor fault diagnosis is presented. In [8], a fault diagnosis method based on discrete wavelet transform is proposed for diagnosis and severity determination of the broken bars both in the transient and steady conditions. Although many researchers have been devoted the diagnosis of the BRBs for many years, there are still some difficulties with regard to the broken bars diagnosis and determination of specific sub-bands with narrow bandwidth without the attendance of other faults [12]. To alleviate this inconvenience, wavelet packet transform (WPT), owning to its more detailed decomposition capability, has been employed for analysis of the signal obtained from condition monitoring of the machine. On the other hand, as the wavelet transform is a time-scale domain technique, it does not provide frequency information on the characteristic feature components.