Optimally robust redundancy relations for failure detection in uncertain systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Fault diagnosis of machines via parameter estimation and knowledge processing: tutorial paper
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Fault diagnosis for nonlinear systems using a bank of neural estimators
Computers in Industry - Special issue: Soft computing in industrial applications
Automatica (Journal of IFAC)
Computational Intelligence in Fault Diagnosis
Computational Intelligence in Fault Diagnosis
Regularized fast recursive least squares algorithms for finitememory filtering
IEEE Transactions on Signal Processing
Additive Change Detection in Nonlinear Systems With Unknown Change Parameters
IEEE Transactions on Signal Processing
Particle filtering based likelihood ratio approach to faultdiagnosis in nonlinear stochastic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hierarchical Fault Diagnosis and Health Monitoring in Satellites Formation Flight
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Nonlinear system fault diagnosis based on adaptive estimation
Automatica (Journal of IFAC)
Synthetic approach to optimal filtering
IEEE Transactions on Neural Networks
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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.