Induction motors (IMs) are essential components in industrial applications. These motors\nhave to perform numerous tasks under a wide variety of conditions, which affects performance and\nreliability and gradually brings faults and effciency losses over time. Nowadays, the industrial\nsector demands the necessary integration of smart-sensors to effectively diagnose faults in these\nkinds of motors before faults can occur. One of the most frequent causes of failure in IMs is\nthe degradation of turn insulation in windings. If this anomaly is present, an electric motor can\nkeep working with apparent normality, but factors such as the effciency of energy consumption\nand mechanical reliability may be reduced considerably. Furthermore, if not detected at an early\nstage, this degradation could lead to the breakdown of the insulation system, which could in turn\ncause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel\nmethodology and its application in a smart-sensor to detect and estimate the healthiness of the\nwinding insulation in IMs. This methodology relies on the analysis of the external magnetic field\ncaptured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete\nwavelet transform (DWT) is used to decompose the signal into different approximation and detail\ncoeffcients as a pre-processing stage to isolate the studied fault. Then, due to the importance of\ndiagnosing stator winding insulation faults during motor operation at an early stage, this proposal\nintroduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an\neffcient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using\ntwo inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple\nsensor. The utility of these sensors is demonstrated through the results obtained from analyzing six\nsimilar IMs with differently induced severity faults.
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