**5. Conclusions**

Recently proposed techniques can detect one single induction motor fault with an adequate certainty; however, most of them rely on the combination of complex mathematical operations that require specific hardware and software for their implementation. Furthermore, they involve the monitoring and processing of different signals as electric current supply and multi-axis vibration signals to obtain time, frequency, and even time– frequency features that allow them to attain high certainty on the induction motor diagnosis. Therefore, in this work, a straightforward technique for multiple IM fault detection, which just requires the computation of homogeneity and kurtosis from a single phase of the supplied electrical current signal during the SCIM startup transient, was introduced. The obtained results from experimental studies demonstrated that the proposed methodology provides highly reliable results on detecting and classifying distinct induction motor faults by computing the homogeneity and kurtosis on the time domain, allowing the identification of five different operational conditions, a motor in healthy state (HLT), a motor with one broken bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with outer-race damage on its bearing (BRN), and a motor with an unbalanced mechanical load (UNB), with remarkable certainty. A thorough comparison against the state of the art in the subject of induction motor fault detection showed that the proposed method outperformed previous approaches in the reviewed literature, which usually just detect one single type of fault, by implementing a low-cost computational technique suitable for online applications.

Future work will focus on assessing the proposed technique for multiple IM fault detection under different scenarios. It will contemplate incorporating additional faulty conditions and signal examination techniques to recover other signal characteristics, as well as assessing distinct kinds of classifications to carry out the fault identification and sorting with high precision.

**Author Contributions:** A.L.M.-H. and E.R.F.-A. performed the methodology implementation and helped during experimentation for data acquisition. L.M.L.-C. helped during data analysis and result interpretation. R.I.M.-C. helped during the manuscript writing—review and editing. M.L.-R. helped during experimentation and data acquisition. E.C.-Y. supervised the project and the experimentation implementation, assessed the obtained results, and helped in the document preparation. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** This work was supported in part by the National Council on Science and Technology (CONACYT), Mexico, under Grants 443689 and 710888, and in part by DAIP—U. de Gto. under the Convocatoria Institucional de Inves-tigacion Cientifica 2022.

**Conflicts of Interest:** The authors declare no conflict of interest.

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