Abnormal condition monitoring method of cement pre-decomposition process based on PCA and AutoEncoder network

The accurate monitoring of abnormal production conditions in cement process is the basis of intelligent control, which is of great significance to improve the intelligent level of cement production. In this paper, an abnormal condition monitoring method of cement pre-decomposition process based on PCA and AutoEncoder network is proposed. In order to improve the accuracy of anomaly detection, this paper integrates the PCA and AutoEncoder algorithms. Firstly, the anomaly detection models based on PCA and AutoEncoder are constructed. On this basis, anomaly monitoring thresholds based on PCA and AutoEncoder are determined. For the samples to be tested, the PCA-based and AutoEncoder-based reconstruction errors are obtained and compared with the reconstruction error thresholds. If any of the judgments are abnormal, the test sample is in an abnormal state. Finally, taking the kiln tail crusting condition in the cement pre-decomposition link as an example, the feasibility of the proposed method is verified. The identification results shows that for the 440 test samples, only 4 abnormal points are not identified for the abnormal working conditions of kiln tail crusting. This paper provided a feasible method for abnormal condition monitoring of cement pre-decomposition process.