Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction

Abstract

Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered. Firstly, the collected signals of vibration and loading force, corresponding to typical faults of coal mill, are decomposed by variational model decomposition, and the intrinsic model functions at different scales are obtained. Then, the eigenvectors consisting of feature energy and sample entropy in these functions are respectively calculated, and the kernel principal component analysis is used for noise removal and dimensionality reduction. Finally, the kernel extreme learning machine model is trained and tested with the dimension reduced feature vector as input and the corresponding coal mill state as output. The results show that the variational model decomposition extraction can improve the input features of the model compared with the single eigenvector model, and the kernel principal component analysis method can significantly reduce the information redundancy and the correlation of eigenvectors, which can effectively save time and cost, and improve the prediction performance of the model to some extent. The establishment of this model provides a new idea for the study of coal mill fault diagnosis.

Keywords

• variational model decomposition
• kernel principal component analysis
• kernel extreme learning machine
• coal mill
• fault diagnosis