Classification of Induction Motor Fault and Imbalance Based on Vibration Signal Using Single Antenna’s Reactive Near Field

Dutta, Sagar; Basu, Banani; Talukdar, Fazal A.

doi:10.1109/tim.2021.3108230

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Other recent works have frequently proposed methods, including neural networks such as CNN and deep convolutional neural networks (DCNNs), for bearing fault classification [59][60][61]. Some of the pros and cons of vibration-based analysis are summarized in Figure 9.…”

Section: Vibrationsmentioning

confidence: 99%

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

et al. 2023

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Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.

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Section: Vibrationsmentioning

confidence: 99%

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

et al. 2023

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“…The frequent voltage abnormalities cause for the malfunction of these devices, which in turn causes huge losses and low working hours. The possible failure of the IMs may be identified in advance by continuous monitoring of IMs various parameters such as vibration [5], acoustic [6], [7], flux, and eddy current, current signature [8]- [13]. Due to the variation of the supply voltage to the motors, the motor vibration amplitude changes.…”

Section: Introductionmentioning

confidence: 99%

A non-invasive technique for monitoring supply voltage variation to single phase induction motor using doppler UWB radar signal analysis

Barusu,

Rani,

Reddy

et al. 2024

IJPEDS

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The induction motors (IMs) are important components of various industries. The condition of IMs monitored continuously using contact and non-contact methods continuously. The contact methods are expensive, complex and difficult to implement. This paper proposes a non-contact method of fault identification in the single-phase induction motor operating in different conditions by analyzing doppler ultra-wide band (UWB) radar signal. The UWB radar generates a high frequency signal, which is transmitted on to the Induction motor form transmitter and software phase locked loop (SPLL) condition received signal from the receiver. The PLL implements as low pass filter, receives reflected signal from an induction motor along with high frequency noise. The received signal filtered to remove the high frequency noise and filtered output is analyzed to identify the different faults such as over voltage faults and under voltage faults when the motor is running with high, medium and low speed. The proposed non-invasive method has advantages compared to other such as the sensor’s sensitivity will not affect with motor temperature and accuracy will not change with position of the sensor and presence of other machines.

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“…In the feature extraction stage of traditional motor fault diagnosis research, signal processing methods, including time-domain [12][13][14], frequency domain [5,[15][16][17][18], and time-frequency domain [19][20][21][22][23], are commonly employed to analyze the measured signals and extract fault features associated with different states. However, the above methods often have problems of low fault diagnosis accuracy and a wide range of applications, and the related research has the limitation of extracting the detailed features of the signals in a single dimension only.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion

Wang,

Ma,

Meng

et al. 2023

Sensors

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Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start–brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors.

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Classification of Induction Motor Fault and Imbalance Based on Vibration Signal Using Single Antenna’s Reactive Near Field

Cited by 9 publications

References 24 publications

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

A non-invasive technique for monitoring supply voltage variation to single phase induction motor using doppler UWB radar signal analysis

Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion

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