Hybrid fault diagnosis of nonlinear systems using neural parameter estimators

• Published in: Neural networks Vol. 50; pp. 12 - 32
• Main Authors: Sobhani-Tehrani, E., Talebi, H.A., Khorasani, K.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-02-2014; Elsevier
• Subjects: Adaptative systems; Algorithms; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer Simulation; Connectionism. Neural networks; Control theory. Systems; Exact sciences and technology; Fault diagnosis; Fundamental areas of phenomenology (including applications); Humans; Hybrid approach; Modelling and identification; Models, Theoretical; Neural estimators; Neural Networks (Computer); Nonlinear Dynamics; Nonlinear systems; Physics; Solid dynamics (ballistics, collision, multibody system, stabilization...); Solid mechanics; Fault diagnosis; Hybrid approach; Nonlinear systems; Neural estimators; Wheel; Reaction force; Non linear control; Solid dynamic; Satellite; Modeling; Adaptive method; Delay; Transients; Fault tolerance; Three axis machine; Observer; System identification; State estimation; Fault tolerant system; Capability index; Closed loop; Transient response; Neural network; Non linear system; Closed feedback; Artificial intelligence; Fault diagnostic; Defect detection; Signal to noise ratio
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2013.10.005

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.