A robust fault detection scheme with an application to mobile robots by using adaptive thresholds generated with locally linear models

This paper proposes a neurofuzzy approach to fault detection in linear systems. The system diagnosed is described by using a neurofuzzy model called LimNet that consists of a linear model and multiple local linear models with interpolation of a "fuzzy basis function". Fault detection is considered in two cases: when faults occur in the linear model part, a KDI-based robust fault detection is applied, where a multi-local-model part is treated as error due to nonlinear undermodeling; when faults occur in the multi-local-model part, a multi-model based fault detection method is developed, in which the identified LimNet is interpreted as several local ARMAX models, and KDI is used as an index to discriminate between each local model and its reference. This paper mainly concentrates discussions on multi-model based fault detection.

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Computationally intelligent strategies for robust fault detection, isolation, and identification of mobile robots

Neurocomputing ◽

10.1016/j.neucom.2015.06.050 ◽

2016 ◽

Vol 171 ◽

pp. 335-346 ◽

Cited By ~ 19

Author(s):

F. Baghernezhad ◽

K. Khorasani

Keyword(s):

Fault Detection ◽

Mobile Robots ◽

Isolation And Identification ◽

Robust Fault Detection

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Design of Robust Fault Detection Scheme for Penicillin Fermentation Process

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.09.590 ◽

2015 ◽

Vol 48 (21) ◽

pp. 589-594 ◽

Cited By ~ 2

Author(s):

Abdul Rehman Khan ◽

Abdul Qayyum Khan ◽

Muhammad Taskeen Raza ◽

Muhammad Abid ◽

Ghulam Mustafa

Keyword(s):

Fault Detection ◽

Fermentation Process ◽

Detection Scheme ◽

Robust Fault Detection ◽

Penicillin Fermentation

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Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

Mathematical Problems in Engineering ◽

10.1155/2015/705725 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Bingyong Yan ◽

Huifeng Wang ◽

Huazhong Wang

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Multiobjective Optimization ◽

Optimization Problem ◽

Multiobjective Optimization Problem ◽

System Uncertainty ◽

Detection Scheme ◽

Gain Matrix ◽

Robust Fault Detection ◽

Simulation Results

A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA) for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.

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