Collaborative Apportionment Noise-Based Soft Sensor Framework

Recently, feature extraction based soft sensor techniques have developed rapidly in the control, optimization, and detection processes of industrial production. However, the raw data obtained from the complex industrial processes are often contaminated by noise, which significantly impacts the results of soft sensor models. We introduce the collaborative apportionment noise (CAN) method based on the density peaks clustering (DPC) theory, based on which, we have proposed a CAN-based soft sensor framework (CAN-SSF) and designed an example model called the CAN-based convolutional neural networks (CAN-CNN) model for industry data prediction. In the CAN method, we determined the magnitude and direction of the noise by the bias degree and deviation of the data. And then the noise is collaboratively apportioned by the credibility degree of the data. Finally, to further explore the feasibility of the CAN method, we added a hyperparameter called reduction degree and conducted two groups of independent experiments for the example model CAN-CNN. The results have shown that the adaptability and stability of the CAN method are higher than the traditional wavelet transform denoising (WT) and denoising autoencoders (DAE). In addition, the prediction performance of the proposed CAN-SSF is better than the traditional CNN and Stacked autoencoders (SAE) models to solve the industrial soft sensor problems.