The Application of Fuzzy Neural Network Algorithm in Plc Fault Diagnosis

In complex industrial scenarios, due to signal noise interference, equipment aging and other reasons, PLC diagnosis accuracy is insufficient and real-time performance is limited. The input layer of the fuzzy neural network (FNN) diagnosis model proposed in this paper performs normalization preprocessing on multi-channel sensor signals; the fuzzification layer uses Gaussian membership function to realize dynamic fuzzy division of fault characteristics; the rule reasoning layer initializes the fuzzy rule matrix based on the expert knowledge base; the neural network layer introduces the improved Elman neural network structure; the output layer uses the weighted average method to realize the defuzzification judgment of the fault type. The highest diagnostic accuracy of FNN is 98.2 %, the average false alarm rate is 0.38 %, and the response time is greatly reduced, providing a highly robust and low-latency intelligent diagnosis solution for industrial PLC systems.