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Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors
Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance),...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111285/ https://www.ncbi.nlm.nih.gov/pubmed/30126208 http://dx.doi.org/10.3390/s18082720 |
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author | Na, Wonbin Park, Changwoo Lee, Seokjoo Yu, Seongo Lee, Hyeongcheol |
author_facet | Na, Wonbin Park, Changwoo Lee, Seokjoo Yu, Seongo Lee, Hyeongcheol |
author_sort | Na, Wonbin |
collection | PubMed |
description | Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device. |
format | Online Article Text |
id | pubmed-6111285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61112852018-08-30 Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors Na, Wonbin Park, Changwoo Lee, Seokjoo Yu, Seongo Lee, Hyeongcheol Sensors (Basel) Article Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device. MDPI 2018-08-18 /pmc/articles/PMC6111285/ /pubmed/30126208 http://dx.doi.org/10.3390/s18082720 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Na, Wonbin Park, Changwoo Lee, Seokjoo Yu, Seongo Lee, Hyeongcheol Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors |
title | Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors |
title_full | Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors |
title_fullStr | Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors |
title_full_unstemmed | Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors |
title_short | Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors |
title_sort | sensitivity-based fault detection and isolation algorithm for road vehicle chassis sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111285/ https://www.ncbi.nlm.nih.gov/pubmed/30126208 http://dx.doi.org/10.3390/s18082720 |
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