Process Monitoring Based On Linear Dynamic System And Its Application In The Preparation Of Ternary Cathode Materials

The high output and high quality requirements of modern industrial processes often make the system structure complex and changeable.For example,the ternary cathode material preparation and sintering process often consists of multiple temperature sections,each temperature section contains multiple temperature zones,and the structure of each temperature zone is different.Not the same.The multi-variable and changing working conditions of complex industrial processes make it difficult to capture system faults,and the safety and stability of production face major challenges.As a risk early warning mechanism in the production and preparation process,process monitoring plays an important role in ensuring production safety and improving product quality.However,data in complex systems presents new characteristics,such as dynamics and multimodality,which make traditional monitoring methods unable to accurately detect process anomalies and frequent industrial accidents.In this paper,the dynamic and multi-modal characteristics presented in the complex system process data are studied,and the monitoring method is proposed and applied to the preparation process of ternary cathode materials.The research content and innovation points of the paper mainly include:(1)Aiming at the problem that it is difficult to model the high-order dynamic characteristics of process data,a dynamic process monitoring method based on Dynamic Autoregressive Latent Variable Model(DALM)is proposed.First,the trend similarity algorithm is used to learn the dynamic order of the model,and the DALM is obtained by extending the first-order Markov chain of continuous latent states in the linear dynamic system to the high-order state chain.Second,the mathematical expectation maximization(EM)algorithm is used to learn the DALM parameters,and the Bayesian filtering and smoothing algorithm is used to solve the posterior distribution of the E-step high-dimensional latent variables.Then,the latent state features of the samples are extracted online through Bayesian filtering and the T~2 statistic is constructed to realize process monitoring.(2)Aiming at the problem of high false alarm rate of single-modal monitoring methods caused by many stable working conditions in the production process,a multi-modal process monitoring method based on factor dynamic autoregressive latent variable model(FDALM)is proposed.First,an improved affine propagation clustering algorithm is used to learn model modal factors,and FDALM is obtained by combining multiple high-order latent state Markov chains through factor modeling technology.Secondly,on the basis of DALM parameter identification,the Lagrange multiplier formula is additionally constructed to update the factor coefficients in the M step by using the constraints of the factors.Then,the state output of the sample in each mode is fused into the posterior failure probability of the sample through Bayesian inference online,so as to give full play to the overall monitoring effect of the multi-modal model.(3)Relying on the monitoring method mentioned above,further carry out the application research on the preparation and production process of ternary cathode materials.According to the high-order dynamics of the process data brought by the interconnection of the spatial structure of each temperature zone,DALM is established for the sintering temperature and product quality variables.On this basis,considering the problem of many stable working conditions in the sintering process caused by the dynamic changes of the external environment,FDALM is established for the process data.Based on the established model,the abnormality and shutdown faults of temperature increase/decrease in the process are monitored.The results show that the overall fault false alarm rate and detection rate of DALM are increased by 33.7%and 21.8%,respectively,compared with the first-order dynamic monitoring method.Compared with DALM,the overall fault false alarm rate and detection rate of FDALM are increased by 48.2%and 1.9%,respectively,which verifies the effectiveness of the proposed monitoring method in the actual sintering process.