Correlation Analysis of Sensor Fault Based on Fuzzy Petri Net and Apriori Algorithm

Due to the complex internal structure of the sensor, the corresponding fault causes are also diverse. Once a fault occurs, the cause of the fault is difficult to determine. This paper proposes a sensor fault correlation analysis method combining fuzzy Petri net (FPN) and Apriori algorithm. First, obtain the typical fault type waveform of the sensor according to the method of fault simulation, calculate its fault waveform characteristics, find out the residual between it and the normal waveform characteristics, and normalize the residual; then, use the modeling method of FPN to establish the correlation analysis model between fault types, fault characteristic indicators and fault modes; finally, the establishment of model weights and transition threshold parameters is achieved through the Apriori algorithm based on association rules. The maintainer can analyze the fault correlation of the sensor through the abnormal waveform of the sensor to preliminarily judge the fault cause, to achieve the purpose of improving the efficiency of maintenance.