Multiscale Kernel-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM

Song, Qiang; Wang, Mingsheng; Lai, Wuxuan; Zhao, Sifang

doi:10.3390/s22186870

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…When an ITSC fault occurs in a PMSM, no matter where the shorting point is in a coil, the wires in the corresponding slot will be shorted accordingly, as the red wires are shown in Figure 1a [28,29]. Figure 1a presents a cross-sectional view of a PMSM with an 8-pole, 36-slot, and concentrated winding structure.…”

Section: Itsc Fault In Pmsmmentioning

confidence: 99%

On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM

Wang,

Song,

Lai

2023

Sensors

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The early detection of an inter-turn short circuit (ITSC) fault is extremely critical for permanent magnet synchronous motors (PMSMs) because it can lead to catastrophic consequences. In this study, a model-based transfer learning method is developed for ITSC fault detection. The contribution can be summarized as two points. First of all, a Bayesian-optimized residual dilated CNN model was proposed for the pre-training of the method. The dilated convolution is utilized to extend the receptive domain of the model, the residual architecture is employed to surmount the degradation problems, and the Bayesian optimization method is launched to address the hyperparameters tuning issues. Secondly, a transfer learning framework and strategy are presented to settle the new target domain datasets after the pre-training of the proposed model. Furthermore, motor fault experiments are carried out to validate the effectiveness of the proposed method. Comparison with seven other methods indicates the performance and advantage of the proposed method.

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Section: Itsc Fault In Pmsmmentioning

confidence: 99%

On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM

Wang,

Song,

Lai

2023

Sensors

Self Cite

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“…PMSMs are crucial electromechanical energy converters that play a significant role in diverse industrial applications due to their performance advantages, including light weight, reliable operation, low noise and high efficiency [1][2][3][4]. Typical applications in automated production lines include packaging machines, drilling machines, cutting machines, and injection moulding machines.…”

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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Multiscale dilated denoising convolution with channel attention mechanism for micro-seismic signal denoising

Cai,

Duan,

Wang

et al. 2024

J Petrol Explor Prod Technol

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Denoising micro-seismic signals is paramount for ensuring reliable data for localizing mining-related seismic events and analyzing the state of rock masses during mining operations. However, micro-seismic signals are commonly contaminated by various types of complex noise, which can hinder micro-seismic accurate P-wave pickup and analysis. In this study, we propose the Multiscale Dilated Convolutional Attention denoising method, referred to as MSDCAN, to eliminate complex noise interference. The MSDCAN denoising model consists of an encoder, an improved attention mechanism, and a decoder. To effectively capture the neighborhood features and multiscale features of the micro-seismic signal, we construct an initial dilated convolution block and a multiscale dilated convolution block in the encoder, and the encoder focuses on extracting the relevant feature information, thus eliminating the noise interference and improving the signal-to-noise ratio (SNR). In addition, the attention mechanism is improved and introduced between the encoder and decoder to emphasize the key features of the micro-seismic signal, thus removing the complex noise and further improving the denoising performance. The MSDCAN denoising model is trained and evaluated using micro-seismic data from Stanford University. Experimental results demonstrate an impressive increase in SNR by 11.237 dB and a reduction in root mean square error (RMSE) by 0.802. Compared to the denoising results of the DeepDenoiser, CNN-denoiser and Neighbor2Neighbor methods, the MSDCAN denoising model outperforms them by enhancing the SNR by 2.589 dB, 1.584 dB and 2dB, respectively, and reducing the RMSE by 0.219, 0.050 and 0.188, respectively. The MSDCAN denoising model presented in this study effectively improves the SNR of micro-seismic signals, offering fresh insights into micro-seismic signal denoising methodologies.

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Multiscale Kernel-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM

Cited by 8 publications

References 39 publications

On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM

On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM

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

Multiscale dilated denoising convolution with channel attention mechanism for micro-seismic signal denoising

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