Modeling Large-Scale Industrial Processes by Multiple Deep Belief Networks With Lower-Pressure and Higher-Precision for Status Monitoring

Typically, fault detection using deep learning is performed based on the features extracted from only one well-trained deep model. However, our results show that large-scale data is complicated and originates from different schemas, which will cause great pressure on deep neural networks, furthermore, the quality of the extracted features will be affected, and the training complexity and time will also be increased. Conversely, deep models would feel comfortable to extract features from raw data that contain less complex relationships and the quality of extracted features are higher and more representative. Hence, variables from large-scale industrial processes in this study are reasonably divided into various schemas with simple relationships by mutual information. Then, the corresponding deep belief network (DBN) models are established under a lighter pressure state to sufficiently extract the abstract and high-order information from data in each schema. Experimental analysis shows that the training efficiency, the accuracy of extracted features and the monitoring performance based on the proposed model system are all better than using only one DBN. What's more, a comparison with those of representative and state-of-the-art methods on numerical and Tennessee Eastman processes also demonstrates the high performance of the proposal called M-DBN.