This paper presents a adaptive dynamic programming-based fault detection and isolation (FDI) scheme to detect and isolate faults in an aircraft jet engine. To this end, the weights in Actor-Critic neural networks are first tuned to learn the input-output map of the jet engine considering its multiple working modes. The convergences of the trainings in Critic-Actor neural networks are strictly proved without knowing the drift dynamics and the input dynamics in the presence of unknown nonlinearities and approximation errors. Using the residuals that are generated by measuring the difference of each network output and the measured engine output, various criteria are established for accomplishing the fault diagnosis task, that addresses the problem of fault detection and isolation of the system components. A number of simulation studies are carried out for combustion chamber of a single-spool jet engine to demonstrate and illustrate the advantages, capabilities, and performance of our proposed fault diagnosis scheme.

本文提出了一种基于自适应动态编程的故障检测和隔离 (FDI) 方案，用于检测和隔离飞机喷气发动机中的故障。为此，首先调整 Actor-Critic 神经网络中的权重以学习考虑其多种工作模式的喷气发动机的输入-输出图。在存在未知非线性和近似误差的情况下，在不知道漂移动力学和输入动力学的情况下，严格证明了 Critic-Actor 神经网络中训练的收敛性。使用通过测量每个网络输出和测量的发动机输出的差异产生的残差，建立各种标准来完成故障诊断任务，解决了系统组件的故障检测和隔离问题。