ArticleEntropy (Basel, Switzerland)2023
Corn Harvester Bearing Fault Diagnosis Based on ABC-VMD and Optimized EfficientNet.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- An Enhanced Hybrid SVD-DBO-VMD Framework for Rolling Bearing Fault Feature Extraction Using Vibration Data.Sensors (Basel, Switzerland) · 2026Article
- Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning.Scientific reports · 2025Article
- Variable Filtered-Waveform Variational Mode Decomposition and Its Application in Rolling Bearing Fault Feature Extraction.Entropy (Basel, Switzerland) · 2025Article
- Motor Fault Diagnosis Based on Convolutional Block Attention Module-Xception Lightweight Neural Network.Entropy (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
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Abstract
The extraction of the optimal mode of the bearing signal in the drive system of a corn harvester is a challenging task. In addition, the accuracy and robustness of the fault diagnosis model are low. Therefore, this paper proposes a fault diagnosis method that uses the optimal mode component as the input feature. The vibration signal is first decomposed by variational mode decomposition (VMD) based on the optimal parameters searched by the artificial bee colony (ABC). Moreover, the key components are screened using an evaluation function that is a fusion of the arrangement entropy, the signal-to-noise ratio, and the power spectral density weighting. The Stockwell transform is then used to convert the filtered modal components into time-frequency images. Finally, the MBConv quantity and activation function of the EfficientNet network are optimized, and the time-frequency pictures are imported into the optimized network model for fault diagnosis. The comparative experiments show that the proposed method accurately extracts the optimal modal component and has a fault classification accuracy greater than 98%.
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