## 1 Introduction  
Electrical machines have been showing their influential role in industrial and domestic applications since the second industrial revolution. This role is evident in electricity generation, such as wind power plants or electrical to mechanical energy converters, which are driving the industry. In various electrical machines, induction motors are ubiquitous because of their simple structure, good efficiency and easy maintenance. A variety of applications makes them consume more than fifty percent of the total energy generated worldwide. The mechanically moving parts and harsh industrial environment make them vulnerable to faults. The electrical faults are mostly related to the stator, such as inter-turn short circuits, phase drop, voltage imbalance, earthing and inverter-related defects. However, the mechanical faults are mainly associated with the rotor, such as broken bars, bad bearings, eccentricity, broken end rings or inadequate foundations. All those faults are degenerative, making it crucial to detect them at the developing stage to avoid catastrophic situations. Various fault diagnostic techniques are available in the literature, such as vibration analysis, thermal analysis, acoustic analysis, electromagnetic field inference, leakage flux, infrared light detection and chemical analysis. However, motor current signature analysis (MCSA)-based fault diagnostic techniques are gaining heightened popularity because of their noninvasive nature and lower complexity. Moreover, a vast field of data processing techniques improves their flexibility and makes them more reliable. It is a well-studied fact that almost all rotor faults modulate the stator currents with specific frequency components. The detection of those frequency components can lead to the cause of the fault. The Fourier transform can be considered a foundation stone for all advanced signal processing techniques. The majority of MCSA-based methods depend on a fast Fourier transform (FFT) of the signal; for example, in [1], the authors used the FFT on the active and reactive currents of a motor to investigate the broken rotor bars and load oscillations. The authors of [2] used the FFT in conjunction with [PERSON]'s vector to make an artificial ants clustering technique for the fault diagnostics of an induction motor. The [PERSON]'s vector makes an entire domain of condition monitoring of electrical equipment [3; 4; 5; 6; 7], giving promising results if used in conjunction with FFT. In [8], the autoregressive method relied on a discrete-time Fourier transform (DTFT) and a notch filter. The researchers in [9] used the FFT to prove that slot harmonics could be used as potential indicators to detect broken rotor bars. In [10], the authors used an adoptive notch filter and FFT for broken rotor bar fault diagnostics of an induction motor. In [11], the FFT was used on simulations and practical results to investigate broken rotor bars and mechanical vibrations. In [12], [PERSON] used the FFT to study the stator current frequency spectrum for different fault conditions, and in [13], the FFT was used along with a band-stop filter for the detection of broken rotor bar frequencies. However, there are certain limitations of the FFT, putting a question mark on its reliability for fault diagnostics. These limitations include spectral leakage, which is the power of the main components leaking into the subsequent frequency bins. If the acquired signal's length and sampling frequency are not good enough, the faults representing frequency components are highly likely to be buried under the corresponding primary frequency bins. This problem becomes worse when the motor is working under no-load or lesser-load conditions, as the faulty frequencies are the function of slip. The other issue of the FFT is that the signal should be in a steady state regime, be stationary and not have any discontinuities. These problems are becoming worse as the inverters are coming forward as an integral part of the drive system.