A model-based probabilistic approach for fault detection and identification with application to the diagnosis of automotive engines

1999 ◽  
Vol 44 (11) ◽  
pp. 2200-2205 ◽  
Author(s):  
L. Dinca ◽  
T. Aldemir ◽  
G. Rizzoni
Keyword(s):  
Fault Detection ◽  
Probabilistic Approach ◽  
Automotive Engines ◽  
Model Based ◽  
Detection And Identification ◽  
Fault Detection And Identification

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.

scholarly journals Online Model-Based Stator-Fault Detection and Identification in Induction Motors

2009 ◽  
Vol 56 (11) ◽  
pp. 4671-4680 ◽  
Author(s):  
C.H. De Angelo ◽  
G.R. Bossio ◽  
S.J. Giaccone ◽  
M.I. Valla ◽  
J.A. Solsona ◽  
...  
Keyword(s):  
Fault Detection ◽  
Induction Motors ◽  
Model Based ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Stator Fault

scholarly journals Model-Based Fault Detection and Identification for Switching Power Converters

2017 ◽  
Vol 32 (2) ◽  
pp. 1419-1430 ◽  
Author(s):  
Jason Poon ◽  
Palak Jain ◽  
Ioannis C. Konstantakopoulos ◽  
Costas Spanos ◽  
Sanjib Kumar Panda ◽  
...  
Keyword(s):  
Fault Detection ◽  
Power Converters ◽  
Switching Power ◽  
Model Based ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Switching Power Converters
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