Sobhani-Tehrani E, Talebi H A, Khorasani K
Globvision, Inc, Canada.
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Neural Netw. 2014 Feb;50:12-32. doi: 10.1016/j.neunet.2013.10.005. Epub 2013 Nov 5.
This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements.
本文提出了一种新颖的集成混合方法，用于非线性系统的故障诊断（FD），该方法利用了系统的数学模型和计算智能技术的自适应非线性逼近能力。与大多数FD技术不同，所提出的解决方案在一个统一的诊断模块中同时完成故障检测、隔离和识别（FDII）。该解决方案的核心是一组与一组单参数故障模型相关联的自适应神经参数估计器（NPE）。NPE不断估计未知的故障参数（FP），这些参数是系统中故障的指示器。开发了两种NPE结构，串并联结构和并联结构，并具有各自独特的理想属性。并联方案对测量噪声具有极强的鲁棒性，并且拥有更简单但更可靠的故障隔离逻辑。相比之下，串并联方案显示出较短的FD延迟，并且对由于控制命令变化引起的闭环系统瞬态具有鲁棒性。最后，设计了一个容错观测器（FTO），以扩展两个NPE的能力，这两个NPE最初假设系统具有完整的状态测量，但实际系统只有部分状态测量。所提出的FTO是一种神经状态估计器，即使在存在故障的情况下也能估计未测量的状态。估计状态和测量状态随后构成所提出的两种FDII方案的输入。在存在干扰和噪声的情况下，对三轴稳定卫星的反作用轮进行FDII的仿真结果证明了所提出的FDII解决方案在部分状态测量下的有效性。
