A model fusion optimization strategy for lithium mill equipment state prediction

Abstract

Abstract Improving the ability and accuracy of intelligent state prediction of large and complex equipment is one of the important directions of current intelligent operation and maintenance technology research. Due to the influence of insufficient analysis of equipment degradation characteristics, single function of traditional prediction model, and difficulty in determining the optimal parameters of the model make the prediction effect poor. In this paper, a state prediction model fusion optimization strategy is proposed for lithium mill equipment as an example. Based on the process flow and vibration mechanism, the inherent vibration characteristics of the roller bearing system are analyzed, and the degradation characteristics of the roller bearing under resonance conditions are explored from the finite element equivalent model, so as to determine the equipment operation stage and the starting point of degradation. The state prediction task is divided into degradation phase and residual life prediction phase, and Time-Convolutional Denoising Autoencoder (TCDAE) and two-layer Sparse Auto Encoder (SAE) are designed for data feature enhancement and degradation feature fusion and dimensionality reduction. Construct BO-BiGRU state prediction model to mine the feature information hidden in the whole time series of data points and adjust the model parameters adaptively using Bayesian Optimization method. The novelty of this study is to analyze the degradation characteristics of key components, correct the theoretical degradation starting point by using the degradation trend formula, and establish a unified framework from monitoring data to condition prediction. Compared with the original model constructed by the above algorithm, the fusion model proposed in this paper has significantly improved performance. The data analysis shows that the prediction accuracy after model fusion is substantially improved, and the accuracy after TCDAE feature enhancement is improved by about 10.2%, the accuracy after two-layer SAE model fusion and dimensionality reduction improved by about 9.8%, and the state accuracy after BO-BiGRU model improved by about 11.6%. The crux to the research depends on the construction of a state prediction model, which is based on the analysis of the bearing degradation process and the effective integration of algorithms. Predictive maintenance of critical components also improves product quality.