Neuro-fuzzy uncertainty de-coupling: a multiple-model paradigm for fault detection and isolation

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

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Fault Detection and Isolation Algorithm for Autonomous Underwater Vehicles Using Interacting Multiple-Model Multiple Estimator

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2015.4704 ◽

2015 ◽

Vol 12 (12) ◽

pp. 5693-5704

Author(s):

Hongyang Shi ◽

Qiuying Wang ◽

Wei Gao ◽

Jian Yang ◽

Yalong Liu

Keyword(s):

Fault Detection ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Fault Detection And Isolation ◽

Interacting Multiple Model ◽

Multiple Model

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Fault Detection and Isolation Using Dynamic Co-Active Neuro-Fuzzy Systems

IFAC Proceedings Volumes ◽

10.3182/20090630-4-es-2003.00083 ◽

2009 ◽

Vol 42 (8) ◽

pp. 498-503

Author(s):

Letitia Mirea

Keyword(s):

Fault Detection ◽

Fuzzy Systems ◽

Fault Detection And Isolation ◽

Neuro Fuzzy

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Fault Detection and Isolation for an Uncertain Takagi-Sugeno Fuzzy System using the Interval Approach

Handbook of Research on Advanced Intelligent Control Engineering and Automation - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-7248-2.ch013 ◽

2015 ◽

pp. 364-389 ◽

Cited By ~ 1

Author(s):

Hassene Bedoui ◽

Atef Kedher ◽

Kamel Ben Othman

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Fuzzy System ◽

Fault Detection And Isolation ◽

Upper And Lower Bounds ◽

Multiple Model ◽

Interval Approach ◽

System Uncertainties ◽

Takagi Sugeno ◽

Detection And Localization

This work deals with the fault detection and localization in the case of uncertain nonlinear systems. The presented method uses the diagnosis based on mathematical models. To model nonlinear systems, the multiple model approach is used. This method uses the Takagi-Sugeno fuzzy systems principle to obtain a nonlinear system named multiple models. This modeling principle has the advantage of obtaining a general model that can describe any class of nonlinear systems. This modeling principle also allows one to obtain the generalization of many results that are already obtained for linear systems to the nonlinear systems. To model the system uncertainties, the interval approach is used because the faults or disturbances are generally unknown, but it is possible to know their upper and lower bounds. The proposed technique is insensitive to measurement uncertainties and highly reliable in case of a fault affecting the outputs system.

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