Estimation of an incipient fault using an adaptive neurofuzzy sliding-mode observer

Abstract A fault, especially an incipient fault has to be detected as early as possible to avoid serious damage occurring in the controlled system. A fuzzy relational sliding mode observer (FRSMO) is proposed to estimate the magnitude of slowly evolving faults in information-poor and non-linear systems. To reduce modelling errors, an on-line learning fault identification scheme is used to update the model and identify the fault in a periodical mode. The performance of the proposed methods is evaluated using a cooling-coil subsystem of an air-conditioning plant in a simulated environment. The simulation results of the actuator fault and flow reduction fault estimation confirm the effectiveness of the proposed methods.

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