Fault diagnosis of PLC-based discrete event systems using Petri nets - Yongyao Li; Ya Wang; Guanghui Zhu; Li Yin; Huimin Zhang (Measurement + Control, 2022)
This paper addresses the fault diagnosis problem of PLC-based systems that can be modeled as Petri nets under a certain level of abstraction. The existing Petri-net-based fault diagnosis approaches often associate transitions and/or places with sensors and require that any change in sensor readings needs to be treated by a PLC, leading to a situation that the PLC would be too busy processing the changes in sensor readings to perform other tasks. This paper assumes that a PLC does not monitor the changes of readings of sensors all the time, but periodically reads the values of sensors when needed. The system output is defined as a marking sequence interleaved with possible observed transitions. A fault diagnosis algorithm is developed by defining and solving integer linear programing (ILP) problems whose size is regardless of the length of the system output. The proposed approach enjoys high computational efficiency compared with other ILP-based approaches and is more suitable for fault diagnosis of PLC-based systems with low computing power.