A Fault Diagnosis and Prediction Model for Power Equipment in Cigarette Factories Based on Deep Learning

The production efficiency and product quality of cigarette factories are related to the economic benefits and market competitiveness of enterprises. The purpose of this article is to study the fault diagnosis and prediction model of power equipment in cigarette factory based on deep learning (DL) to ensure the stable operation of cigarette factory production. Aiming at the problems of traditional fault diagnosis methods, such as relying on manual experience and low diagnosis efficiency, a fault diagnosis and prediction model based on DL is constructed. In the method, firstly, the operation data of power equipment in cigarette factory are collected and converted into a format suitable for DL model input by pretreatment. Then, a fault diagnosis and prediction model based on DL is constructed. With the help of training and optimization, the model can accurately identify the fault types of equipment and give early warning. The results show that the model has achieved high performance in fault diagnosis of power equipment in cigarette factory. The model also has the ability of self-learning and optimization, which can adapt to the change of equipment running state and continuously improve the diagnostic performance.