Abstract:
Stable operation of the sintering process is critical to ensuring the final quality of ternary cathode materials. However, the strong nonlinearity and dynamics, resulting...Show MoreMetadata
Abstract:
Stable operation of the sintering process is critical to ensuring the final quality of ternary cathode materials. However, the strong nonlinearity and dynamics, resulting from the process feedback control, coupled temperature zones, and the unstable external environment, often lead to the traditional process-monitoring methods monitoring poor performance. To this end, this article proposes a recurrent variational autoencoder (RVAE)-based industrial process-monitoring method. First, the variational autoencoder-based nonlinear dynamic system (NDS) model of the sintering process is learned, which can fully extract the process nonlinearity. Then, the autoregressive equation among the latent variables (LVs) is established according to a recurrent neural network (RNN), and the weights to samples at various times are assigned by the weighted moving average method. On this basis, the dynamic characteristics among the LVs can be excavated effectively. Subsequently, some appropriate monitoring statistics are designed based on principal component analysis (PCA), which are more sensitive to faults than indicators based on reconstruction errors. Correspondingly, the RVAE-based process-monitoring strategy is proposed. Finally, the proposed method has been verified to be effective and superior by the industrial application of the sintering process.
Published in: IEEE Transactions on Instrumentation and Measurement ( Volume: 72)