Reducing fault sensitivity of microprocessor-based systems by modifying workload structure

2002 ◽  
Author(s):  
D. Audet ◽  
S. Masson ◽  
Y. Savaria
Keyword(s):  
Fault Sensitivity

Exploring the Relations between Fault Sensitivity and Power Consumption

2013 ◽  
pp. 137-153 ◽  
Author(s):  
Yang Li ◽  
Sho Endo ◽  
Nicolas Debande ◽  
Naofumi Homma ◽  
Takafumi Aoki ◽  
...  
Keyword(s):  
Power Consumption ◽  
Fault Sensitivity

scholarly journals Fault Sensitivity Analysis

2010 ◽  
pp. 320-334 ◽  
Author(s):  
Yang Li ◽  
Kazuo Sakiyama ◽  
Shigeto Gomisawa ◽  
Toshinori Fukunaga ◽  
Junko Takahashi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Fault Sensitivity

scholarly journals Design of a Fault Detection and Isolation System for Intelligent Vehicle Navigation System

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Wei Huang ◽  
Xiaoxin Su
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Intelligent Vehicle ◽  
Sensor Faults ◽  
Driver Assistance ◽  
Isolation System ◽  
Extended Kalman Filters ◽  
Vehicle Navigation System ◽  
Fault Sensitivity ◽  
Sensitivity Pattern

This paper deals with the design of a fault detection and isolation (FDI) system for an intelligent vehicle, a vehicle equipped with advanced driver assistance system (ADAS). The ADASs are outfitted with sensors for acquiring various information about the vehicle and its surroundings. Since these sensors are sensitive to faults, an efficient FDI system should be developed. The designed FDI system is comprised of three parts: a detection part, a decision part, and a fault management part. The detection part applies a generalized observer scheme (GOS). In the GOS, there is bank of extended Kalman filters (EKFs), each excited by all except one sensor measurement. The residual generated from the measurement update of each EKF is therefore sensitive to all sensor faults but one. This way, the fault sensitivity pattern of the residual makes it possible to detect a fault and locate the faulty sensor. The designed FDI system has been implemented and tested off-line with actual experiment data. Good results have been obtained with diagnosing individual sensor faults and outputting fault-free vehicle states.

Sign in / Sign up

Export Citation Format

Share Document