Research on Deep Residual Network-Based Failure Prediction for Cigarette Manufacturing Equipment

Due to the issues of inaccurate failure prediction, slow response times, and high maintenance costs in cigarette manufacturing equipment operations, this paper presents a failure prediction algorithm based on a deep residual network. First, according to the relevant information on cigarette manufacturing and the deep residual network model, a failure prediction system is built to obtain the structural information of the data acquisition module, the preprocessing module, and the fault identification module. Second, to enhance the precision of failure prediction, this paper introduces a strategy for model training and parameter optimization. Third, this paper constructs a deep learning model that effectively considers real-time data and accuracy, thereby providing a more accurate description of the operating state of cigarette manufacturing equipment based on its data characteristics. Finally, this study builds a failure prediction system based on a deep residual network algorithm. Simulation and experimental verification prove the proposed algorithm's effectiveness, significantly improving the accuracy and timeliness of failure prediction and reducing maintenance costs.