Abstract
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.
Highlights
In the companies, electric motors have gained great importance, and have been widely used as electromechanical devices for the conversion of energy, consuming more than 60% of all the energy of any industrial nation [1]
The is implemented in an field-programmable gate array (FPGA) ininorder to generate smart-sensor, which is achieved developing methodology is implemented an FPGA
Provide the smart-sensor with the capability to automatically diagnose the health of the winding. These toolsspecifically provide the smart-sensor capability to automatically diagnose the health of the insulation, before incipient with faultsthe progress into irreversible damage to the motor, making the winding insulation, before incipient faults progress into irreversible damage to the motor, smart-sensor an excellent device for the online diagnosis of winding insulation degradation
Summary
Electric motors have gained great importance, and have been widely used as electromechanical devices for the conversion of energy, consuming more than 60% of all the energy of any industrial nation [1]. It is of paramount importance to study the main faults in induction motors, and there is a clear necessity to develop emergent techniques that can detect faults in the early stages, and to integrate new technologies. In this regard, some authors have adopted the concept of a smart-sensor, in which one or more primary sensors are combined with a processing unit in order to gather certain functionalities like processing, communication and integration. Smart-sensors have found an application in different research fields, including the monitoring and diagnosis of faults in distinct industrial applications [2,3], real-time high-resolution frequency measurement [4], identification of broken bars and unbalance in induction motors [5,6], among others
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