Novel complex-valued long short-term memory network integrating variational mode decomposition for soft sensor

Abstract

Industrial process data is complex time series data including trend and periodicity. However, the existing soft sensor models only focus on the time series context. Therefore, a novel complex-valued long short-term memory (CVLSTM) integrating the variational mode decomposition (VMD) (CVLSTM-VMD) is proposed for the soft sensor. The VMD decomposes industrial process data into binary sequences containing trend and periodic signals, which are input into the bidirectional CVLSTM module to extract the trend and periodic features. Then, the global average pooling fuses trend and periodic features, and the complex-valued fully connected layer learns the dynamic relationship between the fused feature and the key indicator for the soft sensor. Finally, the CVLSTM-VMD is applied in the actual polypropylene process and the purified terephthalic acid (PTA) process. The experiment results show that the proposed CVLSTM-VMD can achieve 93% and 99% prediction accuracies in the polypropylene and the PTA datasets, respectively. Moreover, the CVLSTM-VMD can play a vital role in product quality control and new product development.