Research on AGV Complex Fault Feature Extraction and Accurate Diagnosis Model Based on Set Membership Estimation

With the deep popularization of AGV in the industrial field, the accuracy and timeliness of its fault diagnosis have become the key to ensure efficient and stable production. This paper constructs an innovative fault diagnosis model based on set membership estimation, the core of which is the adaptive dynamic set membership estimation algorithm (ADSE). The ADSE algorithm dynamically adjusts the set membership range based on the real-time operation data, historical fault information and system status of AGV, and automatically adapts to the changes in fault characteristics by using methods such as sliding window statistical analysis and adaptive threshold adjustment. The experiment built a simulated AGV system, simulated and injected various fault types such as motor short circuit and battery power mutation, collected multiple types of sensor data, and used them for testing after cleaning and normalization. The results show that in motor fault diagnosis, the accuracy of the ADSE algorithm reaches 98%, which is 15% higher than the traditional Bayesian network diagnosis algorithm; the accuracy of battery fault diagnosis is 95%, which is 12% higher than the support vector machine algorithm. The average calculation time of the algorithm is only 0.5 seconds, and the resource consumption is low. This research result provides strong technical support for the accurate diagnosis of complex faults of AGVs and has broad application prospects.