Noise reduction method of shearer’s cutting sound signal under strong background noise

Li, Changpeng; Peng, Tianhao; Zhu, Yanmin; Lu, Shuqun

doi:10.1177/00202940221091547

Cited by 9 publications

(5 citation statements)

References 30 publications

(32 reference statements)

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“…SVD is a matrix decomposition method that decomposes a matrix into the product of three matrices: an orthogonal matrix, a diagonal matrix, and the transpose of another orthogonal matrix. SVD is commonly used in signal processing for tasks such as data dimensionality reduction, signal denoising, and feature extraction [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. In signal denoising, SVD aims to eliminate noise by constructing a matrix that contains the signal information and then decomposing this matrix into a series of singular values and corresponding singular vectors representing the time–frequency subspaces.…”

Section: Related Theory and Methodsmentioning

confidence: 99%

“…Unlike CEEMDAN, ICEEMDAN incorporates white noise as part of the complete noise ensemble instead of directly adding Gaussian white noise. SVD is a matrix decomposition method [ 25 , 26 , 27 ] that decomposes and transforms matrices, allowing the collected signals to be decomposed into a series of superimposed linear components. It can effectively detect subtle information variations in signals under complex backgrounds and is widely used in denoising and feature extraction [ 28 , 29 , 30 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD

Zhang

Guo

Yuan

et al. 2023

Sensors

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Vibration monitoring and analysis play a crucial role in the fault diagnosis of hydroelectric units. However, accurate extraction and identification of fault features from vibration signals are challenging because of noise interference. To address this issue, this study proposes a novel denoising method for vibration signals based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), permutation entropy (PE), and singular value decomposition (SVD). The proposed method is applied for the analysis of hydroelectric unit sway monitoring. Firstly, the ICEEMDAN method is employed to process the signal and obtain several intrinsic mode functions (IMFs), and then the PE values of each IMF are calculated. Subsequently, based on a predefined threshold of PE, appropriate IMFs are selected for reconstruction, achieving the first denoising effect. Then, the SVD is applied to the signal after the first denoising effect, resulting in the SVD spectrum. Finally, according to the principle of the SVD spectrum and the variation in the singular value and its energy value, the signal is reconstructed by choosing the appropriate reconstruction order to achieve the secondary noise reduction effect. In the simulation and case analysis, the method is better than the commonly used wavelet threshold, SVD, CEEMDAN–PE, and ICEEMDAN–PE, with a signal-to-noise ratio (SNR) improvement of 6.9870 dB, 4.6789 dB, 8.9871 dB, and 4.3762 dB, respectively, and where the root-mean-square error (RMSE) is reduced by 0.1426, 0.0824, 0.2093 and 0.0756, respectively, meaning that our method has a better denoising effect and provides a new way for denoising the vibration signal of hydropower units.

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Section: Related Theory and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD

Zhang

Guo

Yuan

et al. 2023

Sensors

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“…This method can be used to accurately extract different frequency components of bearing fault vibration signals and diagnose different bearing fault locations. Li et al [34] proposed a combined noise-reduction method for cut acoustic signals based on improved adaptive noise fully integrated Empirical Mode decomposition (ICEEMDAN) and singular value decomposition (SVD). The proposed method can effectively eliminate the influence of background noise in the signal and retain the characteristic frequency of the original cutting sound signal.…”

Section: Status Of Research On Fault Classification and Identificationmentioning

confidence: 99%

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

Han

Liu

et al. 2023

Mathematics

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The bearing fault diagnosis of petrochemical rotating machinery faces the problems of large data volume, weak fault feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint noise reduction, mutual dimensionless metrics and MPGA-SVM approach for rotating machinery bearing fault diagnosis. Firstly, we propose an improved joint noise-reduction method of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding. Moreover, the noise-reduced data are processed by mutual dimensionless processing to construct a mutual dimensionless index sensitive to bearing fault features and complete the fault feature extraction of the bearing signals. Furthermore, we design experiments on faulty bearings of multistage centrifugal fans in petrochemical rotating machinery and processed the input data set according to ICEEMDAN-wavelet threshold joint noise reduction and mutual dimensionless indexes for later validation of the model and algorithm. Finally, a support vector machine model used to effectively identify the bearing failures, and a multi-population genetic algorithm, is studied to optimize the relevant parameters of the support vector machine. The powerful global parallel search capability of the multigroup genetic algorithm is used to search for the penalty factor c and kernel parameter r that affect the classification performance of the support vector machine. The global optimal solutions of c and r are found in a short time to construct a multigroup genetic algorithm-support vector machine bearing fault diagnosis and identification model. The proposed model is verified to have 95.3% accuracy for the bearing fault diagnosis, and the training time is 11.1608 s, while the traditional GA-SVM has only 89.875% accuracy and the training time is 17.4612 s. Meanwhile, to exclude the influence of experimental data on the specificity of our method, the experimental validation of the Western Reserve University bearing failure open-source dataset was added, and the results showed that the accuracy could reach 97.1% with a training time of 14.2735 s, thus proving that the method proposed in our paper can achieve good results in practical applications.

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“…By preprocessing echo signals with EVMD, coal-rock interfaces can be efficiently identified. Li et al [15] proposed a joint denoising method for cutting sound signals based on improved adaptive noise complete ensemble empirical mode decomposition and singular value decomposition, thereby enhancing the performance of coal-rock recognition based on sound signals. Ding et al [16] used the Melfrequency cepstral coefficients method to extract the acoustic emission signal features of coal and rock samples, achieving the identification of coal and rock by employing backpropagation neural network.…”

Section: Introductionmentioning

confidence: 99%

Interpretable coal-rock cutting vibration recognition with Markov transition field and selective neural networks

Wang,

Zhang,

Cao

et al. 2024

Meas. Sci. Technol.

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To address the low accuracy of current one-dimensional signal recognition for coal-rock cutting vibration and the low efficiency of traditional static neural networks, this paper proposes an interpretable recognition method that combines Markov Transition Field (MTF) and channel-selective neural networks. Firstly, by decomposing and reconstructing the energy of wavelet packets and denoising the signal, it is transformed into MTF images with temporal correlation to improve signal processing efficiency. Then, a channel selective module is proposed to replace traditional convolutional layers, enhancing the model's ability to extract data features. A spatial shift mechanism is improved to alleviate the problem of weight degradation and improve the model's generalization capability. The test results on a self-constructed experimental dataset show that this method achieves a recognition accuracy of up to 97.375% without increasing additional parameters, significantly outperforming traditional methods and maintaining good robustness in noisy environments. This study improves the efficiency and accuracy of coal-rock cutting state recognition and provides new ideas for signal processing in environments with limited underground computing resources.

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Noise reduction method of shearer’s cutting sound signal under strong background noise

Cited by 9 publications

References 30 publications

Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD

Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

Interpretable coal-rock cutting vibration recognition with Markov transition field and selective neural networks

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