Soft sensor modeling of mill level based on convolutional neural network
Jie Wei, Lei Guo, Xinying Xu, Gaowei Yan · 2015
A soft sensor model based on Convolutional Neural Network (CNN) is proposed for the measurement of fill level in highly complex environment inside ball mill. CNN has achieved success in the field of image and speech recognition due to the use of local filtering and max-pooling, which is applied to frequency domain in our method to acquire high invariance to signal translation, scaling and distortion. A pair of convolution layer and max-pooling layer is added at the lowest end of neural network as a method to extract the high level abstraction from the vibration spectral features of the mill bearing. Then, the learned features are transferred to the Extreme Learning Machine (ELM) to model the mapping between extracted features and mill level. Experimental results show that the proposed CNN-ELM method can get more accurate and efficient measurement.