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A ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON EXTREME LEARNING MACHINE OPTIMIZED BY IMPROVED WHALE OPTIMIZATION ALGORITHM
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DOI：10.22190/FUME241130008.png)
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Abstract
En 中文
Rolling bearing is one of the most commonly used components in rotating machinery, and researching fault diagnosis techniques for it has important practical significance. In this paper, a fault diagnosis method based on extreme learning machine optimized by improved whale optimization algorithm (IWOA-ELM) is proposed for rolling bearing vibration signals. Firstly, Variational Mode Decomposition (VMD) is used to decompose the vibration signal of the bearing, and the energy entropy is calculated to form the eigenvector. Secondly, based on the original whale optimization algorithm, a hybrid initialization population strategy is adopted to generate an initial population with a certain quality. Selecting convergence factors based on reinforcement learning to improve global search capability, and using adaptive weights and random jumps to update individual positions. In this process, the t-distribution-levy flight variation strategy is introduced to avoid being attracted by local extremum. Then, the improved whale optimization algorithm is used to optimize the input weights and hidden layer thresholds of the Extreme Learning Machine (ELM). Finally, the feature set is input into an improved ELM model for training and testing. Experiments on fault diagnosis of rolling bearings of different types and degrees have shown that the model proposed in this paper can effectively improve the accuracy of fault classification.
Keywords:
Fault diagnosis
Variational mode decomposition
Whale optimization algorithm
Reinforcement learning
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11.8
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301
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