This paper describes the design and implementation of intelligent dynamic models for fault detection and isolation of V94.2(5)/MGT-70(2) single-axis heavy-duty gas turbine system. The series–parallel structure of nonlinear autoregressive exogenous (NARX) models are used for fault detection, which initiate greater robustness and stability against uncertainties and perturbations. Moreover, to improve the fault detection robustness against uncertainties, the Monte Carlo technique is used in the proposed fault detection structure to select the best threshold. The analysis of fault detectability and fault detection sensitivity are accomplished to analyze the performance of the suggested technique. The fault isolation process is also achieved by using the residual classification approach. The results show the feasibly, robustness, and performance of the presented approach for fault diagnosis of nonlinear systems in the presence of uncertainties.

本文介绍了用于 V94.2(5)/MGT-70(2) 单轴重型燃气轮机系统故障检测和隔离的智能动态模型的设计和实现。非线性自回归外生 (NARX) 模型的串并联结构用于故障检测，从而对不确定性和扰动产生更大的鲁棒性和稳定性。此外，为了提高故障检测对不确定性的鲁棒性，在所提出的故障检测结构中使用蒙特卡罗技术来选择最佳阈值。完成了故障检测能力和故障检测灵敏度的分析，以分析所建议技术的性能。故障隔离过程也是通过使用残差分类方法来实现的。结果表明可行、稳健、