Deep Learning for Operating Performance Assessment of Industrial Processes with Layer Attention-Based Stacked Performance-Relevant Denoising Auto-Encoders

The operating performance of a process may degenerate due to process interferences and operation errors, which cancel the benefits of technology design and economic production. Traditional operating performance assessment methods are either lack of real-time due to the post-analysis or difficult to distinguish performance grades for process data with weak differences and strong noise interferences based on shallow learning structures. In this paper, a new layer attention-based stacked performance-relevant denoising auto-encoder (LA-SPDAE) is proposed for the operating performance assessment of industrial processes. It overcomes the defect that the original SDAE ignores task-relevant information in training and only uses the feature of the last hidden layer to complete special tasks. In this study, the original SDAE is improved by optimizing the cross-entropy loss of the performance grade labels in the layer-wise pretraining, which is named stacked performance-relevant denoising auto-encoder (SPDAE), and the performance-relevant features can be extracted under supervision. Moreover, for making good use of performance-relevant features of each layer, they are fused by adaptive weights based on the layer attention mechanism. In the case study of cyanide leaching, the assessment accuracy of the proposed LA-SPDAE model is up to 99.85% under the corrupted proportion of 20%, and the advantage is still maintained as the proportion increases to 80%, which demonstrates the superiority of LA-SPDAE compared with conventional deep neural networks and shallow learning structures.