Acoustic signal analysis for detecting defects inside an arc magnet using a combination of variational mode decomposition and beetle antennae search

Huang, Qinyuan; Xie, Luofeng; Yin, Guofu; Ran, Maoxia; Liu, Xin; Zheng, Jie

doi:10.1016/j.isatra.2020.02.036

Cited by 35 publications

(11 citation statements)

References 54 publications

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“…To verify the advantages of the proposed WConv-LSTM arc magnets internal defective detection model under complex interference conditions compared with traditional machine learning and common end-to-end fault diagnosis methods, we conducted experiments using WPT, HDM, PCA, VMD, WConv-Attention, GRU, Transformer, and WDCNN. Among them, WPT [1], HDM [2], PCA [3], VMD [4] are all traditional machine learning methods for the internal defect detection of arc magnets proposed by our team. WConv-Attention is a hybrid network model that uses the attention mechanism to replace the LSTM of our model.…”

Section: B Performance Comparison With Other Methodsmentioning

confidence: 99%

“…Meanwhile, a growing number of arc magnet manufacturers tend to prefer low-cost detection approaches. Currently, the commonly used detection strategy is to determine whether the internal defects exist through the sensitive human hearing when an arc magnet collides with a mental block, since internal defects are able to change the acoustic and vibration characteristics of an arc magnet being excited [1]- [4]. Such a detection method has low efficiency, poor accuracy, and, more importantly, is easily prone to be errors because of human factors.…”

mentioning

confidence: 99%

“…The nonlinearity and instability of acoustic signals can seriously hinder the extraction and identification of the signal features regarding internal defects, whereas these meaningful features are usually too weak to be discovered. For this reason, Qinyuan Huang, Luofeng Xie, Yue Zhao, Ming Yin et al successively used wavelet packet analysis (WPT) [1], hidden Markov model (HDM) [2], principal component analysis (PCA) [3], variational mode decomposition (VMD) [4], and other technologies process the acoustic signals of the arc magnet and have achieved a better diagnostic result. However, using the above methods to extract features and diagnose defects within arc magnets generally requires complex mathematical operations and a certain understanding of the extracted signals as well as a wealth of signal processing knowledge.…”

mentioning

confidence: 99%

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Combined Convolutional and LSTM Recurrent Neural Networks for Internal Defect Detection of Arc Magnets Under Strong Noises and Variable Object Types

Huang

Zhou

et al. 2021

IEEE Access

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This paper focuses on developing the acoustic-based detection of arc magnet internal defects under different noises and object types. The major challenge in such a detection case is that the traditional approaches mainly rely on expert knowledge and experiential features, which results in a poor generalization performance of the algorithm for new types of detection objects and is susceptible to noise interference. This work presents a novel detection framework based on an end-to-end one-dimensional (1D) convolutional long short-term memory (LSTM) model, where both the spatial and temporal features of the measured acoustic signals are extracted and then jointed for determining the internal defects of arc magnets. In addition, the LSTM layers are employed behind the 1D convolutional neural network, which makes the number of time steps in the LSTM layers for the temporal feature extraction is much smaller than the length of the input segments, thus the computational complexity of the LSTM layers can be highly reduced. Experimental results show that our method is superior to existed methods in the detection accuracy for the internal defects of arc magnets, and the diagnosis time per a single arc magnet is controlled at the millisecond, making it appropriate for real-time applications. Furthermore, the robustness of the proposed framework is validated through experiments on different signal-to-ratios and multiple object types of arc magnets.INDEX TERMS Arc magnets, internal defect detection, one-dimensional convolutional neural network (1D CNN), long short-term memory (LSTM). I. INTRODUCTIONArc magnet is a tile-shaped segment made of magnetic materials, which is used to form a constant magnetic field within permanent magnet motors. Therefore, the performance and life of motors could be significantly affected by the product quality of arc magnetic. However, the complex manufacturing process might produce some defects inside arc magnets. These internal defects, even very subtle, will significantly weaken the mechanical integrity and physical properties of an arc magnet, resulting in severe performance degradation or even damage to the motor. Thus, the internal defect detection of arc magnets is significant and necessary to ensure the performance and safety of motors.

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Section: B Performance Comparison With Other Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Combined Convolutional and LSTM Recurrent Neural Networks for Internal Defect Detection of Arc Magnets Under Strong Noises and Variable Object Types

Huang

Zhou

et al. 2021

IEEE Access

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“…e traditional and existing methods are quite incompetent to enable effective detection. e commonly used detection strategy is to determine whether the internal defects exist through the sensitive human hearing when an arc magnet collides with a mental block, since internal defects are able to change the acoustic and vibration characteristics of an arc magnet being excited [3]. Such a detection method has low efficiency and poor accuracy, and, more importantly, is easily prone to be errors because of human factors.…”

Section: Introductionmentioning

confidence: 99%

Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

Huang

Ran

et al. 2021

Shock and Vibration

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The acoustic-based internal defect detection is essential to ensure the quality of arc magnets efficiently. Swarm decomposition (SWD) is conducive to processing acoustic signals, but it is still confronted with threshold optimization problems. Especially, the existing optimization methods for the SWD thresholds are merely available for a single signal with exclusive characteristics, instead of the various signals with similar characteristics. Therefore, a threshold-optimized SWD using grey wolf optimizer (GWO) is proposed to solve these issues and applied to detect the internal defects of arc magnets. In this method, a fitness function is designed to indicate the relationship between the SWD thresholds and the overall decomposition effect of similar signals. The minimum value of it corresponds to the threshold setting yielding the optimal decomposition. GWO is used for searching such a minimum value, and the obtained optimal threshold setting allows SWD to decompose any signal into a series of oscillatory components. The frequency information in the two oscillatory components with the highest energy ratio is extracted as the internal defect features. Random forest is carried out to identify these features. Experimentally, the detection accuracy reaches above 97%, and the detection speed per single arc magnet does not exceed 3.4 seconds. The proposed method cannot only determine the unified threshold setting of SWD for similar signals but also achieve an accurate, rapid detection for the internal defects.

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“…Moreover, VMD determines the center frequency and bandwidth of each component by iteratively searching and constructing a variational model, which shows good noise immunity and robustness. VMD is widely used in signal processing [25], [26], fault diagnosis [27], [28], wind speed prediction [29], [30] and other fields. But the most prominent shortcoming of VMD is that the decomposition number K value cannot be selected adaptively, and it needs to be preset manually [31], [32].…”

Section: Introductionmentioning

confidence: 99%

A New Denoising Method for Underwater Acoustic Signal

Yang

2020

IEEE Access

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In recent years, the rapid development of marine science has put forward higher and higher requirements for the processing of ship-radiated noise signal. Ship-radiated noise is the noise signal generated by the vibration of various mechanical equipment or the movement of the hull and radiated into the sea when the ship is traveling. Ship-radiated noise signal contains a large number of time-varying, nonlinear and non-stationary components. The denoising processing of ship-radiated noise is the most critical part of underwater acoustic signal processing. In order to more effective reduce the noise of the ship-radiated noise signal, a new denoising method for underwater acoustic signal based on mutual information variational mode decomposition (MIVMD), multivariate multiscale dispersion entropy (mvMDE), and lift wavelet threshold (LWTD) and Savitzky Golay filter (S-G filter), named MIVMD-mvMDE-LWTD-SG, is proposed. Firstly, MIVMD is used to decompose the original signal into n subsignals. Secondly, the mvMDE value of each sub-signal is calculated, and the n sub-signals are divided into high-frequency components and low-frequency components according to the threshold. Then, S-G filter and LWTD method are used to reduce the noise of low-frequency components and high-frequency components respectively. Finally, the low-frequency components and high-frequency components after the denoising processing are reconstructed to obtain the denoising signal. In order to verify the effectiveness of the proposed method, the proposed method is used to reduce the noise of chaotic signal under different signalto-noise ratios (SNR), and compared with the EMD-mvMDE-LWTD and MIVMD-mvMDE-LWTD method. The results show that the proposed method can effective remove the noise in the chaotic signal, better distinguish the adjacent trajectories in the phase space, approximate the real chaotic attractor trajectory, and better retain the useful information in the chaotic signal. The proposed method is further applied to the actual ship-radiated noise signal, and the experimental analysis shows its effectiveness, which lays a solid foundation for further prediction and detection. INDEX TERMS Mutual information variational mode decomposition; multivariate multiscale dispersion entropy; lift wavelet threshold; chaotic signal; denoising

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Acoustic signal analysis for detecting defects inside an arc magnet using a combination of variational mode decomposition and beetle antennae search

Cited by 35 publications

References 54 publications

Combined Convolutional and LSTM Recurrent Neural Networks for Internal Defect Detection of Arc Magnets Under Strong Noises and Variable Object Types

Combined Convolutional and LSTM Recurrent Neural Networks for Internal Defect Detection of Arc Magnets Under Strong Noises and Variable Object Types

Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

A New Denoising Method for Underwater Acoustic Signal

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