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Due to the ubiquitous dynamics of industrial processes, the variable time lag raises great challenge to the high-precision industrial process monitoring. To this end, a process monitoring method based on the dynamic autoregressive latent variable model is proposed in this paper. First, from the perspective of process data, a dynamic autoregressive latent variable model (DALM) with process variables as input and quality variables as output is constructed to adapt to the variable time lag characteristic. In addition, a fusion Bayesian filtering, smoothing and expectation maximization algorithm is used to identify model parameters. Then, the process monitoring method based on DALM is constructed, in which the process data are filtered online to obtain the latent space distribution of the current state, and T2 statistics are constructed. Finally, by comparing with an existing method, the feasibility and effectiveness of the proposed method is tested on the sintering process of ternary cathode materials. Detailed comparisons show the superiority of the proposed method.

machines

A Process Monitoring Method Based on Dynamic Autoregressive Latent Variable Model and Its Application in the Sintering Process of Ternary Cathode Materials