Model-Based Component Fault Detection and Isolation in the Air-Intake System of an SI Engine Using the Statistical Local Approach

2003 ◽  
Author(s):  
Amr Radwan ◽  
Ahmed Soliman ◽  
Giorgio Rizzoni
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Si Engine ◽  
Local Approach ◽  
Intake System ◽  
Model Based ◽  
Air Intake

Model Based Diagnosis of Leaks in the Air Intake System of an SI-Engine

1998 ◽  
Author(s):  
Mattias Nyberg ◽  
Andrej Perkovic ◽  
Lars Nielsen
Keyword(s):  
Si Engine ◽  
Intake System ◽  
Model Based ◽  
Air Intake

A toolbox for model-based fault detection and isolation

1999 ◽  
Author(s):  
L. Tesar ◽  
L. Berec ◽  
G. Dolanc ◽  
G. Szederkenyi ◽  
J. Kadlec ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Model Based

scholarly journals Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Aerospace ◽  
2019 ◽  
Vol 6 (9) ◽  
pp. 94 ◽  
Author(s):  
Matteo D. L. Dalla Vedova ◽  
Alfio Germanà ◽  
Pier Carlo Berri ◽  
Paolo Maggiore
Keyword(s):  
Fault Detection ◽  
Flight Control ◽  
Failure Modes ◽  
Numerical Models ◽  
Short Circuit ◽  
Fault Detection And Isolation ◽  
Model Based ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Functional Components

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.

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