ArticleEntropy (Basel, Switzerland)2021
Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines.
Article in Entropy (Basel, Switzerland), 2021. 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.
- Gear fault diagnosis of wind turbine drivetrains using multi-source information fusion and ensemble learning in a simulation bench study.Scientific reports · 2026Article
- Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG.Entropy (Basel, Switzerland) · 2022Article
- Information Theory and Its Application in Machine Condition Monitoring.Entropy (Basel, Switzerland) · 2022Article
- Application of Generalized Composite Multiscale Lempel-Ziv Complexity in Identifying Wind Turbine Gearbox Faults.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.
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