## 6 Conclusions  
The broken rotor bars fault diagnostics by time-frequency analysis of the motor current under the transient period was investigated in this paper. The detection of fault frequencies in the steady state interval was prevalent in the literature but possessed several difficulties. Since the sideband frequencies were the function of slip, they became challenging to discover under low-load conditions. The spectral leakage of the prevailing supply components was hazardous for the visibility of faulty components, as they were fragile in amplitude. The spectral leakage was the function of the signal length, the sampling frequency and the type of window used to compute the FFT or wavelet.  
Moreover, the inclusion of a vast bandwidth of inverter-fed frequencies made the detection of faulty frequencies hazier. The drive controller's impact on reducing the current ripples was another fact, making fault diagnosis under a steady state regime difficult.  
These drawbacks can be resolved by investigating the health in the transient regime. The transient interval's biggest flaw is the little time, which reduces the resolution of the spectrum. This transient time can be increased by lowering the applied voltage without any external load. By doing so, the broken bars-based V-shaped pattern can be seen with great accuracy. Moreover, the FFT-based drawbacks are considerably reduced by using the wavelet approach. The transient spectrum's legibility is further enhanced by attenuating the fundamental component with an IIR filter's help. The IIR band-stop filter has a sharp transition interval and low passband ripples, having less influence on the remaining frequency components.  
Figure 13: (**a**) The time–frequency plot of the healthy motor with 10% of the rated voltage. (**b**) The time–frequency contour plot. (**c**) The time–frequency plot for 1 BRB motor with 10% of the rated voltage. (**d**) The corresponding contour plot.  
Several spatial and supply-related frequency patterns can cause a false alarm if they are not differentiated accurately from the actual faulty components. Those components are much weaker in amplitude compared with the fault-related frequencies. The use of the filter to attenuate those components is nearly impossible, as they are not stationary. Hence, the regions with the maximum spectral energy in the form of a contour plot are used, which can be easily adjusted as a threshold to show the pattern. The results are presented using the simulations and the measurements taken from a laboratory-based test rig.  
The proposed approach can be used for the pattern-based training of advanced artificially intelligent (AI) diagnostic algorithms.  
Conceptualization, B.A., T.V. and A.B.; methodology, B.A., T.V. and A.B.; validation, A.K., A.R. and A.B.; data curation, B.A. and K.K.; writing--original draft preparation, B.A.; writing--review and editing, B.A., K.K. and A.R.; visualization, T.V. and P.S.G.; supervision, T.V., A.K. and A.B. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results received funding from the EEA/Norway Grants 2014-2021, Industrial Internet methods for electrical energy conversion systems monitoring and diagnostics.  
Not applicable.  
The authors declare no conflict of interest.