**6. Conclusions**

This paper has introduced a novel methodology for the automatic diagnosis of broken rotor bars in soft-started induction motors by means of the information fusion of current and stray flux signals. The proposed methodology relies on a pair of indicators proposed here. Such indicators are based on the arithmetic mean and maximum value, respectively, of specific regions from a time-frequency map; which are obtained by analyzing the current and stray flux signals captured during the start-up transient of the machine. These indicators are based on the fact that rotor faults yield the amplification of specific frequency components, which are found to be slip-dependent; hence, their evolution and amplification can be tracked by the proposed indicators. Additionally, as it can be observed in the results, it is very relevant to combine the information provided by the current and the stray flux signals, since the control mechanisms applied by each soft-starter manufacturer tend

to modify the fault pattern evolutions, depending on the parameters used, according to the specific motor application and particular necessities of the final user. In this regard, the proposed methodology shows an excellent performance in the automatic classification among the studied faults, invariably to the soft-starter used, since an overall performance higher than 94.4% is achieved in any case. Finally, the obtained results show that, by means of the proposed methodology, it is possible to automatically discriminate among a healthy motor, a motor working under one broken rotor bar, and a motor working under two broken rotor bars. The proposal may find a grea<sup>t</sup> applicability under a vast array of applications demanding automated final diagnosis, especially those where the motor is constantly operated under starts/stops by means of a soft-starter.

**Author Contributions:** Conceptualization J.A.A.-D. and R.A.O.-R.; methodology, I.Z.-R., J.A.A.-D. and R.A.O.-R.; software, A.N.-N. and I.Z.-R.; validation, A.N.-N., I.Z.-R. and V.B.-M.; formal analysis, I.Z.-R. and A.N.-N.; investigation, A.N.-N., I.Z.-R. and V.B.-M.; resources, J.A.A.-D. and R.A.O.- R.; data curation, A.N.-N. and V.B.-M.; writing—original draft preparation, A.N.-N. and I.Z.-R.; writing—review and editing, I.Z.-R., J.A.A.-D. and R.A.O.-R.; visualization, I.Z.-R., J.A.A.-D. and R.A.O.-R.; supervision, J.A.A.-D. and R.A.O.-R.; project administration, J.A.A.-D. and R.A.O.-R.; funding acquisition, J.A.A.-D. and R.A.O.-R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Spanish 'Ministerio de Ciencia Innovación y Universidades' and FEDER program in the framework of the 'Proyectos de I+D de Generación de Conocimiento del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i, Subprograma Estatal de Generación de Conocimiento' (ref: PGC2018-095747-B-I00).

**Acknowledgments:** The authors would like to thank Consejo Nacional de Ciencia y Tecnología (CONACyT) under scholarship 652815.

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