scholarly journals Fault Detection and Isolation of an Aircraft Turbojet Engine Using a Multi-Sensor Network and Multiple Model Approach

2014 ◽  
Vol 15 (2) ◽  
Keyword(s):  
Fault Detection ◽  
Sensor Network ◽  
Fault Detection And Isolation ◽  
Multiple Model ◽  
Turbojet Engine ◽  
Model Approach

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Z. N. Sadough Vanini ◽  
N. Meskin ◽  
K. Khorasani
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Data ◽  
Turbine Engines ◽  
Multiple Model ◽  
Jet Engines ◽  
Dynamic Neural Networks ◽  
Autoassociative Neural Networks

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.

scholarly journals Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2332 ◽  
Author(s):  
Vincent Judalet ◽  
Sébastien Glaser ◽  
Dominique Gruyer ◽  
Saïd Mammar
Keyword(s):  
Autonomous Vehicle ◽  
Fault Detection And Isolation ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Actuator Faults ◽  
Robust Identification ◽  
The Road ◽  
Driving Assistance ◽  
Experimental Vehicle ◽  
Model Approach

The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.

Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

2010 ◽  
Author(s):  
N. Meskin ◽  
E. Naderi ◽  
K. Khorasani
Keyword(s):  
Linear Models ◽  
Operating Mode ◽  
Fault Detection And Isolation ◽  
Operating Point ◽  
Hierarchical Architecture ◽  
Multiple Model ◽  
Jet Engines ◽  
Maximum Probability ◽  
Jet Engine ◽  
Model Approach

In this paper, a novel real-time fault detection and isolation (FDI) scheme that is based on the concept of multiple model approach is proposed for jet engines. A modular and a hierarchical architecture is proposed which enables the detection and isolation of both single as well as permanent concurrent faults in the engine. The nonlinear dynamics of the jet engine is linearized in which compressors and turbines maps are used for performance calculations and a set of linear models corresponding to various operating modes of the engine (namely healthy and different fault modes) at each operating point is obtained. Using the multiple model approach the probabilities corresponding to each operating point of the engine are generated and the current operating mode of the system is detected based on evaluating the maximum probability criteria. It is shown that the proposed methodology is also robust to the failure of pressure and temperature sensors and extensive levels of noise outliers in the sensor measurements. Simulation results presented demonstrate the effectiveness of our proposed multiple model FDI algorithm for both structural faults and actuator fault in the jet engine.

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

2015 ◽  
pp. 364-389 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document