An Intelligent Fault Detection Method for Raw Material Grinding of Vertical Cement Mills Using Wavelet Kernel Based Light-weighted Neural Networks

Abstract

The vertical cement mill is widely used in the cement industry due to low power consumption, high energy efficiency, and its compact size. The intelligent fault detection using reducer vibration data for vertical cement mills is of great importance for safe, stable, and efficient operation. In this paper, a deep learning based fault diagnosis method using 1D samples is proposed. For fast and low cost detection, the convolution neural network is improved for real-time implementation. The depthwise separable convolution and inverted residual block are applied for light-weighted network construction. The feature extraction is performed by the wavelet kernel based convolution layer. Three kinds of wavelet basis have been applied, namely Laplace, Mexhat, and Morlet basis. To improve the feature extraction performance of the first convolution layer, different wavelet basis functions are testd. The number of convolution layers is reduced to extract more features. Measured signals from a cement slurry factory in China are used to verify the effectiveness of proposed detection method. The light-weighted network structure provides 75% reduction of the memory space. Compared with conventional cement mill fault diagnosis methods, which acquire high-dimensional data and high frequency sampled signals, the proposed method adopts data measured in a simpler way with compatible detection accuracy.