scholarly journals Model-Based Fault Diagnosis of an Anti-Lock Braking System via Structural Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4468 ◽  
Author(s):  
Qi Chen ◽  
Wenfeng Tian ◽  
Wuwei Chen ◽  
Qadeer Ahmed ◽  
Yanming Wu
Keyword(s):  
Structural Analysis ◽  
Experimental Testing ◽  
Structural Representation ◽  
Safe Driving ◽  
Braking System ◽  
Model Based ◽  
Detection And Identification ◽  
Fault Modelling ◽  
Fault Detection And Identification ◽  
Safety Systems

The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-determined (MSO) sets selection, and MSO-based residual design. In terms of the four faults in the ABS, they are evaluated to be detectable through performing a structural representation and making the Dulmage-Mendelsohn decomposition with respect to the fault modelling, and then they are proved to be isolable based on the fault isolability matrix via SA. After that, four corresponding residuals are generated directly by a series of suggested equation combinations resulting from four MSO sets. The results generated by numerical simulations show that the proposed FDI system can detect and isolate all the injected faults, which is consistent with the theoretical analysis by SA, and also eventually validated by experimental testing on the vehicle (EcoCAR2) ABS.

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 Design and Evaluation of a Structural Analysis-Based Fault Detection and Identification Scheme for a Hydraulic Torque Converter

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4103
Author(s):  
Qi Chen ◽  
Jincheng Wang ◽  
Qadeer Ahmed
Keyword(s):  
Structural Analysis ◽  
Fault Detection ◽  
High Efficiency ◽  
Automatic Transmission ◽  
Torque Converter ◽  
Effect Analysis ◽  
Scheme Design ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Hydraulic Torque Converter

A hydraulic torque converter (HTC) is a key component in an automatic transmission. To monitor its operating status and to detect and locate faults, and considering the high-efficiency fault detection and identification (FDI) scheme design by the methodology of structural analysis (SA), this paper presents an SA-based FDI system design and validation for the HTC. By the technique of fault mode and effect analysis (FMEA), eight critical faults are obtained, and then two fault variables are chosen to delegate them. Fault detectability and isolability, coupled with different sensor placements, are analyzed, and as a result, two speed sensors and two torque sensors of pump and turbine are selected to realize the maximal fault detectability and fault isolability: all six faults are detectable, four faults are uniquely isolable, and two faults are isolated from the other faults, but not from each other. Then five minimal structurally overdetermined (MSO) sets are easily acquired by SA to generate five corresponding residuals. The proposed FDI scheme of the HTC by SA is first validated by a theoretical model, then by an offline experiment in a commercial SUV, and the testing results indicate a consistent conclusion with the simulations and theory analysis.

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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