Three-phase induction motor loading estimation based on Wavelet Transform and low-cost piezoelectric sensors

Ardila-Rey

et al. 2021

Sensors

Milk is an important dietary requirement for many populations due to its high nutritional value. However, increased demand has also made it prone to fraudulent activity. In this sense, scientists have sought to develop simple, low-cost, and portable techniques to achieve quality control of milk in industry and farms as well. This work proposes a new instrumentation system based on acoustic propagation and advanced signal processing techniques to identify milk adulteration by industrial contaminants. A pair of transmitter-receiver low-cost piezoelectric transducers, configured in a pitch-catch mode, propagated acoustic waves in the bovine milk samples contaminated with 0.5% of sodium bicarbonate, urea, and hydrogen peroxide. Signal processing approaches such as chromatic technique and statistical indexes like the correlation coefficient, Euclidian norm and cross-correlation square difference were applied to identify the contaminants. According to the presented results, CCSD and RMSD metrics presented more effectiveness to perform the identification of milk contaminants. However, CCSD was 2.28 × 105 more sensitivity to distinguish adulteration in relation to RMSD. For chromatic clustering technique, the major selectivity was observed between the contamination performed by sodium bicarbonate and urea. Therefore, results indicate that the proposed approach can be an effective and quick alternative to assess the milk condition and classify its contaminants.

Section: Methodsmentioning

confidence: 99%

A New Acoustic-Based Approach for Assessing Induced Adulteration in Bovine Milk

Santos

Ardila-Rey

et al. 2021

Sensors

“…The energy of acoustic signals is a well known metric for fault diagnosis. In TIM monitoring, AE sensors are applied to detect variations in the energy of acoustic signals [14,15]. Atypical variations usually indicate an anomaly.…”

Section: Signal Energymentioning

confidence: 99%

Stator Winding Fault Phase Identification Using Piezoelectric Sensors in Three-Phase Induction Motors

Lucas

Rocha

7th International Electronic Conference on Sensors and Applications

et al. 2020

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.

“…The Wavelet transform allows a simultaneous observation of the wave in frequency for each instant of time of a signal. This type of analysis is as a powerful tool for identifying transients in signals [14,15]. Because partial discharges emit acoustic waves, this approach becomes fundamental in identifying the moment of failure, as well as their frequency spectrum.…”

Section: Wavelet Transformmentioning

confidence: 99%

“…In this article, the Discrete Wavelet Transform (DFT) was applied aiming to perform feature extraction of PD evolution by analyzing the detail and approximation coefficients. The approximation coefficients allow signal analysis in high scale and low frequencies, as opposed to details, which take features in low-scale and high-frequency components [15].…”

Section: Wavelet Transformmentioning

confidence: 99%

An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor

Santos

7th International Electronic Conference on Sensors and Applications

Binotto

et al. 2020

Under normal operation, insulation systems of high voltage electrical devices, like power transformers, are constantly subjected to multiple types of stresses (electrical, thermal, mechanical, environmental, etc.), which can lead to a degradation of the machine insulation. One of the main indicators of the dielectric degradation process is the presence of partial discharges (PD). Although it starts due to operational stresses, PD can cause a progressive insulation deterioration, since it is characterized by localized current pulses that emit heat, UV radiation, acoustic, and electromagnetic waves. In this sense, acoustic emission (AE) transducers are wildly applied in PD detection. The goal is to reduce maintenance costs by predictive actions and avoid total failures. Becasue of the progressive deterioration, the assessment of the PD evolution is crucial for improving the maintenance planning and ensure the operation of the transformer. Based on this issue, this article presents a new wavelet -based analysis in order to characterize the PD evolution. Three levels of failures were carried out in a transformer and the acoustic signals captured by a lead zirconate titanate piezoelectric transducer were processed by discrete wavelet transform. The experimental results revealed that the energy of the approximation levels increasing with the failure evolution. More specifically, levels 4, 6, 7, and 10 presented a linear fit to characterize the phenomena, enhancing the applicability of the proposed approach to transformer monitoring.