2011 IEEE Vehicular Networking Conference (VNC) 2011
DOI: 10.1109/vnc.2011.6117140
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A framework for health monitoring of automotive electrical and electronic control systems (poster)

Abstract: The automotive industry has seen enormous growth in the size and complexity of electrical and electronic system architectures. As complexity increases the problem of diagnosing faults in a vehicle's electrical and electronic systems is becoming increasingly difficult. Many faults are as a result of system-level disturbances or interactions that are difficult to interpret and diagnose using existing component-level diagnostics which have been traditionally focused on mechanical system diagnostics. The problem o… Show more

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Cited by 3 publications
(3 citation statements)
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“…Amor-Segan et al [5] focuses on the automotive industry and proposes a new system level approach to manage the faults in a vehicle networked electronic systems. The framework involves different phases -data collection, data analysis, knowledge discovery, diagnostics or prognostics leading to corrective and preventative intervention.…”
Section: B Strategies/schematics Used In Implementationmentioning
confidence: 99%
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“…Amor-Segan et al [5] focuses on the automotive industry and proposes a new system level approach to manage the faults in a vehicle networked electronic systems. The framework involves different phases -data collection, data analysis, knowledge discovery, diagnostics or prognostics leading to corrective and preventative intervention.…”
Section: B Strategies/schematics Used In Implementationmentioning
confidence: 99%
“…Regardless of the fact that the clear indication of the system degradation is given by the physical parameters, these non-physical values can be used to link some physical parameters to the actual system performance. A framework is proposed in [5] regarding Electronic Control Unit (ECU) to use ECU hardware and software data to asses the health. It is using parameters from the ECU such as ECU reset and initialisation statistics, ECU error counts, function activation statistics, network status and performance statistics.…”
Section: B Non-physical Software Parametersmentioning
confidence: 99%
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