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.

Jingjing Liu

Associate Professor

My research interests include low-power smart micro-sensor integrated circuit design, image sensors, biomedical sensors, and energy harvesting circuits.