A new fault diagnosis method of rolling bearing of shearer
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摘要: 针对基于K-means聚类算法的采煤机滚动轴承故障诊断结果存在不稳定的问题，提出了一种基于TDKM-RBF神经网络的采煤机滚动轴承故障诊断新方法。该方法采用Tree Distribution算法确定K-means聚类算法的初始聚类中心，消除K-means聚类结果的波动性，采用K-means算法确定RBF神经网络的参数，再将训练好的神经网络用于故障诊断。仿真结果表明，该方法的聚类过程迅速，稳定性较高，提高了采煤机滚动轴承故障诊断的正确率。Abstract: In view of unstable problem existed in fault diagnosis result for rolling bearing of shearer based on K-means clustering algorithm, a new fault diagnosis method of rolling bearing of shearer based on TDKM-RBF neural network was proposed. The method adopts Tree Distribution algorithm to determine initial clustering center of the K-means clustering algorithm, so as to eliminate volatility of K-means clustering results. The method uses K-means algorithm to determine the parameters of the RBF neural network, then the trained neural network was used for fault diagnosis. The simulation results show that the method has quick clustering process,higher steability, and obviously improves accuracy of fault diagnosis for rolling bearing of shearer.
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Keywords:
- shearer /
- rolling bearing /
- fault diagnosis /
- volatility /
- RBF /
- K-means clustering algorithm /
- TD algorithm
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期刊类型引用(4)
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