Research on Preventive Maintenance of Industrial Internet Based on Reinforcement Learning

Abstract

In the Industrial Internet, the failure prediction and health management of industrial equipment can help managers to further predict and determine the damage and danger of the current equipment, ensure the safe and stable operation of industrial equipment. A reasonable forecast and management plan can greatly save the cost in the industrial production process and improve the industrial production efficiency. In order to adapt to more complex application scenarios, this paper models multiple devices with different decay rates and their upstream production buffers in a pipeline system. Considering the semi-Markov decision process corresponding to different decay rates, a preventive maintenance strategy based on DDQN is proposed. This strategy can help managers determine the optimal maintenance methods for different types of equipment under the condition of limited resources, achieve the purpose of increasing production and reducing maintenance costs, and has a certain guiding role in solving equipment maintenance problems in the actual production process.