Abstract
Nowadays, industrial processes are fully equipped by redundant hardware sensors which can be interfered by random noises. Hence, it is of high importance to develop soft sensing solutions for key variables prediction and process monitoring. Various methods have been carried out to cope with different data characteristics among which auto-correlation and non-stable features have been considered as two challenging tasks. In this paper, a novel weighted autoregressive dynamic latent variable (WARDLV) model is constructed where a weighted log-likelihood function is created combined with high-order dynamic information extraction techniques. Different weights are designed for state transition and emission in latent space and original space, respectively. Compared with previous researches, global weights are completely redesigned and consisted of different window confidences and local weights. The efficiency of the proposed method is further demonstrated with a real industrial application.
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