## 2 Materials and Methods
### Failure Detection  
To distinguish among the different types of failure, we have taken acoustic samples of a 1.1 kW motor working at full load. The electric motor was a 4-pole machine coupled to a direct current (D.C.) machine acting like a load. In Figure 10, a test-bench and set of tested rotors are shown. Figure 8: The spectrum of the original signal (blue), the spectrum of the noisy signal (black), and the spectrum obtained after applying the wavelet algorithm for noise reduction. Figure 9: Execution time in terms of the number of samples. The execution time was calculated using the Matlab _tictoc_ function and on a computer with an i5 processor and 4 Gb of RAM. The processed signals of a rotor with 28 bars present the following characteristics: (1) healthy, (2) one broken bar, two broken bars in the relative positions 1 and 2, (3) two broken bars in the relative positions 1 and 3, and (4) two broken bars in the relative positions 1 and 5. See Figure 10 for the T-bench of the tested rotors. In each test, we recorded the acoustic noise signal with a conventional smartphone equipped with an internal microphone (type omnidirectional condenser) that enabled us to capture the required signals at a sampling rate of 16 kHz. The sampling window consists of 80,000 data measurements. The sampling window consists of 80,000 data measurements. To prevent any vibration influence in the recording, we have located the microphone was at the same place in all the tests. [23]. We removed the undesired components of the spectrum through the noise-reduction algorithm. Figures 11 and 12 show the signal of the healthy rotor before and after the noise reduction with the harmonic peaks. Figure 11: Healthy rotor spectrum before the noise reduction with the harmonic peaks. Figure 10: T-bench of the tested rotors. We show the results of the one broken bar signal. In Figures 13-15, we show the signal spectrum before and after the noise reduction. With zoom in the range of 500-1200 Hz we see a third harmonic that has not appeared in the healthy signal in Figure 11. To solidify the finding of the increment of harmonic peaks on the broken bar signals, we have also looked for it in the rest of the two broken bar signals. Figure 14: One broken bar signal spectrum after the noise reduction with the harmonic peaks. Figure 12: Healthy rotor spectrum after the noise reduction with the harmonic peaks. Figure 13: One broken bar signal spectrum before the noise reduction with the harmonic peaks. We show the spectrum results for the two broken bars signals in Figures 16-18. Regarding the spectrum of the signal with two broken bars in the relative position 1-2, we note four harmonic peaks, with different frequencies with respect to the healthy motor spectrum. Here, the most prominent harmonic amplitude is twice the highest of the previous signal (healthy and with one broken bar). The four harmonic pattern is also present in the signal corresponding to the two broken bars signals in relative positions 1-5, see Figure 16. However, in the spectrum of the two broken bars signals in relative positions 1-3, we only appreciate two peaks, see Figure 17.  
Figure 9:0 Spectrum of the two broken bars, in relative position 1–2, signal after the noise reduction with the harmonic peaks. Figure 9:1 One broken bar signal spectrum before the noise reduction with zoom in the range of 500–1200 Hz. We note a characteristic component swaying on 750 Hz frequency in all signals. However, we show the differences in the spectrum of the noise-reduced signals. We show a comparison of all signals spectrum after the noise-reduction process in Figure 9:2 We show the characteristics peaks of the different signals together. The novelty of the proposed calculation is that it does not require the frequency component to identify the fault. It is based on the stationary or cycle stationary nature of the acoustic signal and only needs to process the amplitude, frequency, and phase parameters.