Fault detection and accommodation in dynamic systems using adaptive neurofuzzy systems

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Fault detection and accommodation in dynamic systems using adaptive neurofuzzy systems

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Fault detection and accommodation plays a very important role in critical applications. A new software redundancy approach based on an adaptive neurofuzzy system (ANFIS) is introduced. An ANFIS model is used to detect the fault while another model is used to accommodate it. An accurate plant model is assumed with arbitrary additive faults. The two models are trained online using a gradient-based approach. The accommodation mechanism is based on matching the output of the plant with the output of a reference model. Furthermore, the accommodation mechanism does not assume a special type of system or nonlinearity. Simulation studies prove the effectiveness of the new system even when a severe failure occurs. Robustness to noise and inaccuracies in the plant model are also demonstrated.

Inspec keywords: gradient methods; software reliability; fault diagnosis; real-time systems; adaptive systems; fuzzy neural nets; redundancy; learning (artificial intelligence); inference mechanisms

Other keywords: dynamic systems; ANFIS model; neural-fuzzy inference system; output matching; fault accommodation; adaptive system; software redundancy; fault detection; online learning; gradient method

Subjects: Learning in AI (theory); Neural nets (theory); Adaptive system theory; Simulation, modelling and identification; Software engineering techniques

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