Data fusion based on-line product quality evaluation of ternary cathode material cyber-physical systems

Abstract

In the manufacturing process of ternary cathode material, batching, mixing, loading, and sintering procedures play decisive roles in product quality, and data generated by these procedures are collected by the ternary cathode material cyber-physical system (TCM-CPS). However, the data of the process are heterogeneous and have different properties, and sampling of key quality variables is difficult. On this basis, on-line evaluation of product quality is hard. In this study, a data fusion based on-line product quality evaluation method of TCM-CPS is proposed. Data features of each procedure are firstly extracted and fused by combining feature indices and kernel principal component analysis. Then, in order to establish the model between the extracted data features and the key quality variables such as particle size and surface-free lithium content, a semi-supervised double-weighted probabilistic principal component regression is proposed. After that, a distance cost index is brought to cluster and grade the two quality variables, which are predicted by the semi-supervised model. Moreover, the on-line evaluation of product performance is achieved by setting a rule table in accordance with production experience. Finally, an industrial application verifies the proposed method, of which the effective and accuracy can be acquired from results.