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

Zhang, Fangqing; Guo, Jiang; Yuan, Fang; Shi, Yongjie; Li, Zhaoyang

doi:10.3390/s23146368

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…They reconstructed gear vibration signals by selecting Intrinsic Mode Function (IMF) components with larger kurtosis values, achieving signal denoising for gearboxes. Zhang et al [ 7 ] proposed a novel vibration signal denoising method based on an improved adaptive noise complementation system, Empirical Mode Decomposition, permutation entropy, and singular value decomposition. Experimental validation confirmed its effectiveness for the vibration signals of hydroelectric units.…”

Section: Introductionmentioning

confidence: 99%

A New Denoising Method for Belt Conveyor Roller Fault Signals

Hao,

Zhang,

Gao

et al. 2024

Sensors

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In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method’s denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals.

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Section: Introductionmentioning

confidence: 99%

A New Denoising Method for Belt Conveyor Roller Fault Signals

Hao,

Zhang,

Gao

et al. 2024

Sensors

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“…By analyzing and processing vibration signals, it is possible to detect equipment anomalies in a timely manner, predict potential failures, reduce downtime, and enhance the availability and operational efficiency of the units. This ensures their safe and stable operation [6,7]. As the failure of rotating machinery, such as hydroelectric units, will lead to changes in the frequency distribution of the vibration signal, some scholars use the Fourier change as the core of the frequency domain analysis method, the signal from the time domain into the frequency domain, to obtain the spectrum.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis in Hydroelectric Units in Small-Sample State Based on Wasserstein Generative Adversarial Network

Sun,

Zou,

Wang

et al. 2024

Water

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In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address this issue, this paper introduces a novel fault diagnosis method tailored for unbalanced small-sample states in hydroelectric units based on the Wasserstein generative adversarial network (W-GAN). Firstly, the fast Fourier transform is used to convert the signal from the time domain to the frequency domain to obtain the spectral data, and the W-GAN is trained to generate false spectral data with the same probability distribution as the real fault data, which are combined with the actual data and inputted into the 1D-CNN for feature extraction and fault diagnosis. In order to assess the effectiveness of the proposed model, a case study was conducted using actual data from a domestic hydropower plant, and the experimental results show that the sample features can be effectively enriched via data enhancement performed on small-sample data to improve the accuracy of fault diagnosis, which verifies the effectiveness of the method proposed in this paper.

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“…This algorithm effectively reduces the noise residuals of the modes after the EMD decomposition, solves the problem of mode aliasing, and reduces the number of unnecessary modal components [12]. Zhang et al [13] proposes a noise reduction method for vibration signals using joint ICEEMDAN, arrangement entropy, and singular value decomposition. This method has been shown to have a better denoising effect compared to other commonly used methods.…”

Section: Introductionmentioning

confidence: 99%

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

Yan,

Zhuang,

2024

Meas. Sci. Technol.

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The vibration signal associated with the operating process of circuit breakers(CBs) includes a detailed operating status in the formation of the operating mechanism. To effectively extract the characteristic information of vibration effectively for diagnosis and analysis, a new feature extraction method for the CBs operating mechanism is proposed. First, a new denoising method, the improved complete ensemble empirical mode decomposition with adaptive noise-multi-resolution singular value decomposition (ICEEMDAN-MRSVD), is introduced, which can effectively remove the influence of noise on faults. Then, a quantitative method is proposed to extract the characteristic information of the CB, i.e. the variational mode decomposition (VMD)-power spectrum entropy (PSE) is proposed. By using this method, the difference of CB vibration signals in different fault states can be quantified. Through comparative analysis of different recognition models, experiments show that the support vector machine model based on ICEEMDAN-MRSVD noise reduction and VMD-PSE features has a high recognition accuracy of 98.61%, which has high application value.

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

Cited by 7 publications

References 23 publications

A New Denoising Method for Belt Conveyor Roller Fault Signals

A New Denoising Method for Belt Conveyor Roller Fault Signals

Fault Diagnosis in Hydroelectric Units in Small-Sample State Based on Wasserstein Generative Adversarial Network

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

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