Aiming at the problem that it is difficult to model DES(discrete event systems) for large-scale real systems and it is impossible to carry out effective fault diagnosis, this paper proposed a fault diagnosis method based on active learning. Firstly, the method added normal/fault labels to the acquired system event logs, divided the log set into a training set and a test set, and proposed an iterative algorithm based on abstraction technique to extract fault feature samples from the event logs in the training set. Then, it constructed the initial fault identifier from the fault feature samples, and checked the accuracy of identifier using the event logs in the test set. Simulation results show that the fault diagnosis algorithm enabled higher diagnosis accuracy under model unknown. Finally, examples illustrate the application of the fault diagnosis algorithm under system model unknown. Compared with the existing research, the proposed method can diagnose faults when the system model is unknown and the complexity of the algorithm is polynomial, which results in higher diagnostic accuracy and a wider range of applications.

[2023-11-02] Accepted Paper /doc/2023.08.0372/v1

[2024-04-05] Printed Article /doc/2023.08.0372/v2