Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Wang, Jingyue; Li, Jiangang; Wang, Haotian

doi:10.1177/1461348420908364

Cited by 21 publications

(5 citation statements)

References 22 publications

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“…This analysis reveals valuable insights, including hidden periodic patterns and similar spectral peaks within the signal. 26 Moreover, it facilitates the extraction of relevant signals that may be buried in frequency domain noise, 27 covering aspects like frequency range, stability, and noise.…”

Section: Methodsmentioning

confidence: 99%

Acoustic-based approach for micro-leakage detection and localization in water supply pipelines

Feng,

Zhao,

Ran

et al. 2024

Environ. Sci.: Water Res. Technol.

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Section: Methodsmentioning

confidence: 99%

Acoustic-based approach for micro-leakage detection and localization in water supply pipelines

Feng,

Zhao,

Ran

et al. 2024

Environ. Sci.: Water Res. Technol.

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“…The experimental data comes from the output shaft acceleration data obtained by the gearbox fault simulation on QPZZ-II platform. QPZZ-II platform is mainly composed of drive motor, bearing, gearbox, shaft and other brake governor parts (Wang et al, 2020). The specific structure of the experiment platform and the installation mode of the sensor are shown in Figure 2.…”

Section: Data Selectionmentioning

confidence: 99%

A hybrid intelligent gearbox fault diagnosis method based on EWCEEMD and whale optimization algorithm-optimized SVM

Men

et al. 2023

IJSI

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PurposeThis paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.Design/methodology/approachAn intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.FindingsThe fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.Originality/valueIn most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

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“…Mousavi et al [12] applied CEEMDAN to the structural damage detection of steel truss bridges, proving that the detection effect when using CEEMDAN is significantly better than that when using EMD or EEMD. EMD is also used in composite fault diagnosis of gearboxes [13], mechanical fault diagnosis [14], milling chatter detection [15], etc.…”

Section: Literature Review 21 Empirical Mode Decompositionmentioning

confidence: 99%

An Integrated Time Series Prediction Model Based on Empirical Mode Decomposition and Two Attention Mechanisms

Wang,

Dong,

Zhang

2023

Information

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In the prediction of time series, Empirical Mode Decomposition (EMD) generates subsequences and separates short-term tendencies from long-term ones. However, a single prediction model, including attention mechanism, has varying effects on each subsequence. To accurately capture the regularities of subsequences using an attention mechanism, we propose an integrated model for time series prediction based on signal decomposition and two attention mechanisms. This model combines the results of three networks—LSTM, LSTM-self-attention, and LSTM-temporal attention—all trained using subsequences obtained from EMD. Additionally, since previous research on EMD has been limited to single series analysis, this paper includes multiple series by employing two data pre-processing methods: ‘overall normalization’ and ‘respective normalization’. Experimental results on various datasets demonstrate that compared to models without attention mechanisms, temporal attention improves the prediction accuracy of short- and medium-term decomposed series by 15~28% and 45~72%, respectively; furthermore, it reduces the overall prediction error by 10~17%. The integrated model with temporal attention achieves a reduction in error of approximately 0.3%, primarily when compared to models utilizing only general forms of attention mechanisms. Moreover, after normalizing multiple series separately, the predictive performance is equivalent to that achieved for individual series.

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Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Cited by 21 publications

References 22 publications

Acoustic-based approach for micro-leakage detection and localization in water supply pipelines

Acoustic-based approach for micro-leakage detection and localization in water supply pipelines

A hybrid intelligent gearbox fault diagnosis method based on EWCEEMD and whale optimization algorithm-optimized SVM

An Integrated Time Series Prediction Model Based on Empirical Mode Decomposition and Two Attention Mechanisms

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