Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm

Jiang, Wei; Shan, Yahui; Xue, Xiaoming; Ma, Jianpeng; Chen, Zhong; Zhao, Nan

doi:10.3390/e25081111

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…While significant for enhancing the cleaning screen's service life, this study exclusively explores vibration signals without delving into a comprehensive analysis. In reference [26], a fault diagnosis method utilizing composite scale variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is presented, showcasing superiority in balancing datasets but lacking applicability to small sample data. Moving on to reference [27], DDS Adash software is employed for signal processing, utilizing the Demodulation Fast Fourier Transform (FFT) root mean square (RMS) method and the DDS Adash Fault Source Identification Tool (FASIT) technique.…”

Section: Comparison With Other Studies On Similar Topicsmentioning

confidence: 99%

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Xie,

Li,

Liu

et al. 2024

Agriculture

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In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosis method, anchored in data augmentation, aims to discern early faults in agricultural machinery rolling bearings, particularly within an imbalanced sample framework. The methodology involves determining early fault signals throughout the life cycle, constructing early fault datasets with varying imbalance rates for different fault types, and subsequently employing the Synthetic Minority Oversampling Technique (SMOTE) to balance the fault data. The study then extracts relative wavelet packet energy and time-domain sensitive features (variance, peak to peak) from the original and generated fault data to form a multi-domain fault feature vector. This vector is utilized for fault state recognition using a Support Vector Machine (SVM). Evaluation metrics such as accuracy, recall, and F1 values assess the recognition effectiveness for each rolling bearing state, with the overall model recognition evaluated based on accuracy. The proposed method is rigorously analyzed and validated using the XJTU-SY rolling bearing accelerated life test dataset. Comparative analysis is conducted with non-data enhanced fault feature vectors, specifically the relative energy of the wavelet packet, both with and without time-domain features. Experimental results underscore the superior performance of multi-domain fault features in providing a comprehensive description of signal information, leading to enhanced classification performance. Furthermore, the study demonstrates improved classification accuracy and recall rates for the balanced dataset compared to the imbalanced dataset. This research significantly contributes to an effective identification method for the early fault diagnosis of small sample rolling bearings in agricultural machinery.

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Section: Comparison With Other Studies On Similar Topicsmentioning

confidence: 99%

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Xie,

Li,

Liu

et al. 2024

Agriculture

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“…However, the complex structure of the combine harvester results in a complex coupling relationship between vibration signals and serious mutual interference. Moreover, the collected vibration signals exhibit non-smooth and non-periodic characteristics [2]. Lastly, due to the large structural shape of the combine harvester, the installation position of the sensors is far from the location where issues occur, leading to more interference signals.…”

Section: Introductionmentioning

confidence: 99%

A Study of Fault Signal Noise Reduction Based on Improved CEEMDAN-SVD

Zhao,

Ma,

et al. 2023

Applied Sciences

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In light of the challenges posed by the complex structural characteristics and significant coupling of vibration signals in rotating machinery, this study proposes an adaptive noise reduction method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Additionally, an enhanced threshold screening Singular Value Decomposition (SVD) algorithm is introduced to address the issues pertaining to noise identification and feature extraction in the context of vibration signals from rotating machinery, which are subjected to complex noise interference. The effectiveness of the proposed approach is substantiated through the evaluation of key metrics, such as the signal-to-noise ratio (SNR), as well as the utilization of advanced signal analysis techniques, including Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). The experimental results validate the finding that the combination of the improved CEEMDAN and the enhanced threshold screening SVD algorithm effectively reduces noise interference in vibration signals from rotating machinery. This integrated denoising approach successfully preserves the informative characteristics of the vibration signals, thereby laying a foundation for the subsequent fault diagnosis of rotating machinery.

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“…However, the decomposition effect of VMD on the signal is influenced by the preset parameters K and α [26,27]. Intelligent optimization algorithms have been introduced to adaptively search for the values of K and α [28,29]. For example, Zhou et al [30] and Zhang et al [31] used the ant colony algorithm (ACO) and grasshopper optimization algorithm (GOA), respectively, to optimize VMD parameters.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction Method for Rolling Bearings Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Variational Mode Decomposition

Wang,

Li,

et al. 2023

Sensors

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Due to the difficulty in dealing with non-stationary and nonlinear vibration signals using the single decomposition method, it is difficult to extract weak fault features from complex noise; therefore, this paper proposes a fault feature extraction method for rolling bearings based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods. CEEMDAN was used to decompose the signal, and the signal was then screened and reconstructed according to the component envelope kurtosis. Based on the kurtosis of the maximum envelope spectrum as the fitness function, the sparrow search algorithm (SSA) was used to perform adaptive parameter optimization for VMD, which decomposed the reconstructed signal into several IMF components. According to the kurtosis value of the envelope spectrum, the optimal component was selected for an envelope demodulation analysis to realize fault feature extraction for rolling bearings. Finally, by using open data sets and experimental data, the accuracy of envelope kurtosis and envelope spectrum kurtosis as a component selection index was verified, and the superiority of the proposed feature extraction method for rolling bearings was confirmed by comparing it with other methods.

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Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm

Cited by 6 publications

References 48 publications

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

A Study of Fault Signal Noise Reduction Based on Improved CEEMDAN-SVD

Fault Feature Extraction Method for Rolling Bearings Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Variational Mode Decomposition

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