Robust Low-Rank Clustering Contrastive Learning Integrating Transformer for Noisy Industrial Soft Sensors

Abstract

Strong process noise and disturbance caused by operating conditions and internal systems have brought a considerable challenge for soft sensor modeling in industrial processes. Existing modeling methods mainly focus on removing the noise from the process data, which do not improve the accuracy of the soft sensor model effectively for it is unrealistic to get completely noise-free data in actual industrial processes. Therefore, a novel robust Low-rank Clustering Contrastive Learning (LrCCL) integrating Transformer (LrCCL-T) is proposed in this paper. Based on the data augmentation technology, the LrCCL is designed to learn intrinsic and invariant feature representations from the process data by combining low-rank prior (Lr) and adaptive clustering contrastive learning (CCL). The CCL can bring closer the pairs from the same sample and cluster. Moreover, the Lr which assumes that samples in the same cluster should lie in a low-dimensional subspace is utilized to enhance the learned feature representations by adding a low-rank constraint. Then, the Transformer is used to build the soft sensor model, which can extract dynamic temporal relationship between the learned feature representations and outputs. Finally, to verify the effectiveness and robustness of the proposed method, a public industrial thermal power dataset and an actual industrial polypropylene dataset are utilized to build the steam volume (SV) and the melt index (MI) soft sensor model, respectively. The contrastive and ablation experiment results show that the proposed LrCCL-T can achieve comparable accuracy to other state-of-the-art soft sensor methods under strong noise industrial environment.