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

Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a...

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Vydané v:Entropy (Basel, Switzerland) Ročník 25; číslo 8; s. 1111
Hlavní autori: Jiang, Wei, Shan, Yahui, Xue, Xiaoming, Ma, Jianpeng, Chen, Zhong, Zhang, Nan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 25.07.2023
MDPI
Predmet:
Agricultural machinery
Algorithms
Analysis
Artificial intelligence
Bearing strength
Bearings
combine harvester
Combine harvesters
Decomposition
Dispersion
dispersion entropy
Entropy
Farm equipment
Fault diagnosis
Machine learning
Optimization
Research methodology
Roller bearings
rolling bearing
Signal analysis
Signal processing
Vibration
VMD
Wavelet transforms
ISSN:1099-4300, 1099-4300
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Shrnutí:Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e25081111