Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.
Due the complexity of control and automation networks in modern industries, sensor-based systems stand out as effective approaches for failure detection in electrical and mechanical machines. This kind of intervention has a high operational value in industrial scenarios, once it can avoid corrective maintenance stops, i.e., before the failure reaches a high level of severity and compromises the machine. Consequently, the development of sensors applied to non-destructive techniques (NDT) for failure monitoring in electrical machines has become a recurrent theme in recent studies. In this context, this paper investigates the application of low-cost piezoelectric sensors for vibration analysis, which is an NDT that has already proved to be efficient for the detection of many structural anomalies in induction motors. Further, the proposed work presents a low-cost alternative approach for expensive commercial sensors, which will make this NDT more attractive for industrial applications. To describe the piezoelectric sensor frequency response, a pencil lead break (PLB) test was performed. After this validation, the Root Mean Square (RMS) value from the voltage samples obtained in the test bench was used as a signal processing method. A comparison between the results for different levels of mechanical load attached to the machine shaft indicated not only the successful performance of the low-cost sensors for load estimation purposes, but also showed that oversized motors may present higher vibration levels in some components that could cause mechanical wearing.
Three-phase induction motors (TIMs) play a key role in industrial production lines. Due to their robustness and versatility, TIMs are commonly used to drive different devices like fans, conveyors, sieves, and compressors. However, these devices are often exposed to mechanical and electrical faults. Among them, failures in stator winding insulation lead to severe damage to the TIMs and can cause operational interruptions. Therefore, several approaches have been developed to monitor electrical faults in induction motors. The acoustic emission (AE) stands out as an efficient non-invasive technique (NIT) for TIM diagnosis. In this work, the AE analysis was applied to detect winding insulation faults and identify which electrical phase was affected. To achieve this proposal, a TIM was subjected to insulation faults in each of the three electrical phases, and the acoustic signals were acquired by four piezoelectric sensors attached to the motor. These signals were processed using a new technique, which calculates the energy of a specific range of the signal spectrum and assigns the energy values of each piezoelectric sensor to a coordinate axis (x, y). By ploting the values for each fault condition, this technique allows the detection of insulation faults and correctly identifies the affected phase by clustering the resulting values. Finally, the proposed methodology presented satisfactory results in winding insulation diagnosis.
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