Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux
Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands t...
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doaj-6e1a80561a5f41c7ba9b54eb7daa218d2020-11-25T01:33:14ZengMDPI AGEnergies1996-10732019-05-01129165810.3390/en12091658en12091658Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray FluxIsrael Zamudio-Ramirez0Roque Alfredo Osornio-Rios1Miguel Trejo-Hernandez2Rene de Jesus Romero-Troncoso3Jose Alfonso Antonino-Daviu4Engineering Faculty, San Juan del Río Campus, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, C.P. 76808 San Juan del Río, Querétaro, MéxicoEngineering Faculty, San Juan del Río Campus, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, C.P. 76808 San Juan del Río, Querétaro, MéxicoEngineering Faculty, San Juan del Río Campus, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, C.P. 76808 San Juan del Río, Querétaro, MéxicoEngineering Faculty, San Juan del Río Campus, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, C.P. 76808 San Juan del Río, Querétaro, MéxicoInstituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, SpainInduction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.https://www.mdpi.com/1996-1073/12/9/1658induction motorsmart-sensorstray fluxtime-frequency transformswavelet entropy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Israel Zamudio-Ramirez Roque Alfredo Osornio-Rios Miguel Trejo-Hernandez Rene de Jesus Romero-Troncoso Jose Alfonso Antonino-Daviu |
spellingShingle |
Israel Zamudio-Ramirez Roque Alfredo Osornio-Rios Miguel Trejo-Hernandez Rene de Jesus Romero-Troncoso Jose Alfonso Antonino-Daviu Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux Energies induction motor smart-sensor stray flux time-frequency transforms wavelet entropy |
author_facet |
Israel Zamudio-Ramirez Roque Alfredo Osornio-Rios Miguel Trejo-Hernandez Rene de Jesus Romero-Troncoso Jose Alfonso Antonino-Daviu |
author_sort |
Israel Zamudio-Ramirez |
title |
Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux |
title_short |
Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux |
title_full |
Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux |
title_fullStr |
Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux |
title_full_unstemmed |
Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux |
title_sort |
smart-sensors to estimate insulation health in induction motors via analysis of stray flux |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2019-05-01 |
description |
Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults. |
topic |
induction motor smart-sensor stray flux time-frequency transforms wavelet entropy |
url |
https://www.mdpi.com/1996-1073/12/9/1658 |
work_keys_str_mv |
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