Reliability evaluation for manufacturing system based on dynamic adaptive fuzzy reasoning petri net

Due to failure, partial failure, or maintenance, the capacity of each machine is multi-state. Therefore, the limited relationship between the capacity of each machine and the input raw materials has to be considered. Additionally, in order to utilize the machine more effectively, the capacity of the buffers cannot be ignored, too. In this paper, a dynamic adaptive fuzzy reasoning Petri net is proposed to evaluate reliability of a manufacturing system with multiple production lines. Firstly, the model of manufacturing system is conducted, and from the perspective of demand, the minimum capacity vector and loading vector of each machine are determined. Secondly, knowledge representation and rules are formulated to establish weighted fuzzy petri nets. And the weighted fuzzy Petri net is adaptive based on the real-time level of buffers, the minimum capacity vector and loading vector. Moreover, the efficiency of product production can be improved while ensuring system reliability by adjusting the buffer level. Finally, a numerical experiment is used to demonstrate the application of our method.