模型未知的离散事件系统故障诊断方法

针对离散事件系统模型难以建立的大型实际系统，无法对其进行有效故障诊断的问题，提出一种基于主动学习的故障诊断方法。首先，为获取到的系统事件日志添加正常/故障标签，并将日志集划分为训练集和测试集，提出一种基于抽象技术的迭代算法提取训练集中日志的故障特征样本。然后，通过故障特征样本构造初始故障识别器，并利用测试集中的日志检验识别器的准确性。仿真结果表明，该故障诊断算法使得模型未知下诊断精度更高。最后，实例说明系统模型未知下故障诊断算法的应用。与现有研究相比，提出的方法可以在系统模型未知下进行故障诊断且算法复杂度为多项式，诊断精度更高，应用范围更加广泛。

Fault diagnosis of discrete event systems with unknown models

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.