Model-Based Design for Vehicle Health Monitoring

Rossi, Christopher M.; Benson, David A.; Sargent, Russell; Breger, Louis

doi:10.2514/6.2012-2577

Cited by 4 publications

(1 citation statement)

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“…Fault detection is highly dependent on the model's accuracy, in [43] there is developed a method of adjusting properly the component equations to be insensitive to noise and disturbances, but sensitive to faults. Based on the complexity of the modelled system and the model's capability of incorporating noise and disturbances, there have been proposed diagnostic rules based on fixed thresholds [44] or adaptive thresholds [45][46][47].…”

Section: Fault Detection Challengesmentioning

confidence: 99%

A review of model based and data driven methods targeting hardware systems diagnostics

Skliros¹,

Miguez²,

Ali³

et al. 2018

Diagnostyka

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System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls.

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Section: Fault Detection Challengesmentioning

confidence: 99%