Regular inspections and fault detections of electrical machines at early stages are considered essential in order to keep away from partial or total breakdown [
24,
25,
26,
27,
28]. In the last 20 years, fault diagnosis and condition monitoring of electrical machines has gained substantial consideration. In the literature, several fault detection and localization approaches have been suggested. In [
8,
12], Messaoudi et al. applied the classical motor current signature analysis (MCSA) method to multiple fault diagnoses of IM. They improved the efficiency of the MCSA even if the motor operates under abnormal conditions [
8,
12]. In [
7], the authors applied the fast Fourier transform (FFT) method to analyze the phase of the stator current in order to detect one BRB fault in IM operating under a low load. The efficiency of this method is degraded for the detection of partial breakage of the rotor bar. To enhance the diagnosis, the authors employed the Hilbert transform (HT). Pezzani et al. used the active and reactive current Park’s vector instead of the conventional Park’s vector to detect BRBs, even when there was a load oscillation with a frequency close to twice the slip frequency [
11]. Casimir et al. utilized the pattern recognition methods to identify the induction motors’ faults. Then, they selected the most relevant features by applying the sequential backward algorithm [
6]. In [
5], an analytic signal concept method was used with the FFT to estimate the instantaneous angular speed (IAS). They demonstrated that the estimated IAS outperforms conventional vibration in the detection of rotor bar defects and shaft misalignment. Serrano et al. used and compared two artificial neural network methods for stator faults detection in IM, the support vector machine and back-propagation algorithm. By using the MCSA and the spectral Park’s vector, training patterns were obtained [
9]. Pires et al. used the spectral analysis of the IM square current for BRB and rotor eccentricity faults diagnosis. They deduced from the result obtained that this method gives more information about the rotor faults than the classical MCSA approach [
15]. In [
24], the authors used both methods, the FFT and the Grey relational analyses, to enhance diagnosis results. They proved that this combination provides a quick and easy method for the detection of BRBs in IMs. Abd-el-Malek et al. [
19] used the HT to extract the BRB fault signature from the stator current envelope. Then, they developed a formula based on the statistical analysis to determine the exact location of the fault in the IM rotor. In [
22], the authors employed the discrete wavelet transform (DWT) method to analyze different electrical and mechanical control quantities. They deduced that the calculation of the stored energy in each decomposition level gives information about the BRBs’ fault, particularly for the active power energy. In [
26], two monitoring systems were compared, which are type-1 and type-2 fuzzy monitoring systems, in which the robustness and performance were improved for the type-2 fuzzy monitoring system compared to type-1. In [
27], the authors sensed vibration data from multiple MEMS accelerometers and utilized the Q method based on the neural network model forbearing and BRB fault detection in IM. Using the collected data, they developed an efficient method for multiple fault detection. Quiroz et al. utilized the random forests method for the rotor bars fault detection in LS-PMSM [
23]. In their study, during the startup phase of the healthy and faulty motor, the transient current signal was collected, in which high correction rates of diagnosis were reached. In [
17], after the application of elliptic and notch filters on the Park’s vector components, the higher harmonic index was monitored. The technique’s efficiency was proven independently from the induction motor rotor slot number. Glowacz used an acoustic-based fault diagnosis technique of the IM. Then, the nearest neighbor classifier, back-propagation neural network, and the modified classifier based on words coding were used for recognition [
20]. In [
21], the authors employed multiple signal classification techniques for IMs fault diagnosis. They deduced that the current signal square produces additional fault frequency components in the broken rotor bar and easily helps in diagnosing the half BRB fault at normal load conditions. Xie et al. developed a technique based on magnitudes of vibration signal spectrums for BRB detection in SCIM. The results have shown that broken-bar fault has a more direct impact on the electromagnetic force [
25]. In [
14], the authors analyzed two fault signatures: the Hilbert modulus current space vector (HMCSV) and the Hilbert phase current space vector (HPCSV) using the FFT. For all the studied faults, they concluded that the HPCSV spectrum is richer in harmonics than the HMCSV spectrum. Arredondo and his co-workers analyzed two types of signals: acoustic sound signals and vibration signals, to detect BRBs in IMs. They used the complete ensemble empirical mode decomposition to separate the signal into several intrinsic mode functions (IMF). After, they selected the most relevant IMFs to facilitate the BRBs’ fault detection [
18]. In [
28], the authors used the DWT and local binary pattern methods together to detect rotor and bearing faults of three-phase IMs. The support vector machine and the k-nearest neighbor algorithms are used to classify sounds. Both methods reached high classification accuracy. However, to the best of the authors’ knowledge, the combination of the Park’s vector approach (PVA) and extended Park’s vector approach (CPEPVA) have not been considered in any paper. Therefore, further studies should be carried out in order to show their advantages over other methods, which is the subject of this paper.