Two-Level Fault Detection and Isolation Algorithm for Vehicle Platoon

被引:8
|
作者
Wang, Gaochao [1 ]
Ding, Ying [3 ]
Hou, Yandong [1 ,2 ]
Zhou, Yi [1 ,2 ]
Jia, Xiangyi [1 ]
机构
[1] Henan Univ, Sch Comp & Informat Engn, Kaifeng 475004, Peoples R China
[2] Int Joint Res Lab Cooperat Vehicular Networks Hen, Kaifeng 475004, Peoples R China
[3] Henan Univ, Lab & Equipment Management Off, Kaifeng 475004, Peoples R China
来源
IEEE ACCESS | 2018年 / 6卷
基金
中国国家自然科学基金;
关键词
FDI; fleet; structure analysis; virtual force analysis; residual generation; space geometry; COMMUNICATION; SYSTEM;
D O I
10.1109/ACCESS.2018.2815644
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
To deal with the fault of the vehicle platoon, we have established a fault detection and isolation (FDI) system with two-level fault diagnosis architecture. For simplicity, we divide the FDI architecture into two kinds: system failure and component element failure. To detect these faults, we set up the FDI mathematical model of the fleet based on the vehicular spacing, and the sensor FDI model of a certain vehicle. Meanwhile, we construct the state space model of the fleet, and design the residual generator using the space geometry method for system failure. To design the residual generation model of the fleet for component element failure, we strengthen the structure analysis of both the fleet and a certain vehicle. What's more, to elucidate the factors that cause the change of vehicle distance, the virtual force analysis is introduced. Using the adaptive threshold method, it can enhance both the sensitivity of the FDI system to the residual and the robustness to the disturbance. To promote the vehicle itself and the fleet's information perception ability, all vehicles (Autonomous Mobile Robots) are equipped with infrared distance measuring sensors, odometers, a pair of incremental optical encoders, and so on. The experimental results show that the proposed method is reliable and efficient for FDI of fleet.
引用
收藏
页码:15106 / 15116
页数:11
相关论文
共 50 条
  • [31] A greedy algorithm for the two-level nested logit model
    Li, Guang
    Rusmevichientong, Paat
    OPERATIONS RESEARCH LETTERS, 2014, 42 (05) : 319 - 324
  • [32] Implementation of Two-Level SVPWM Algorithm in PSCAD/EMTDC
    Liu, Jiajun
    Yao, Lixiao
    Wu, Tiansen
    An, Yuan
    2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2010,
  • [33] A two-level parallel algorithm for material nonlinearity problems
    Lee, Jeeho
    Kim, Min Seok
    STRUCTURAL ENGINEERING AND MECHANICS, 2011, 38 (04) : 405 - 416
  • [34] An efficient two-level partitioning algorithm for VLSI circuits
    Cherng, JS
    Chen, SJ
    Tsai, CC
    Ho, JM
    PROCEEDINGS OF ASP-DAC '99: ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE 1999, 1999, : 69 - 72
  • [35] Two-level Decomposition Algorithm for Shift Scheduling Problems
    Doi, Tsubasa
    Nishi, Tatsushi
    2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), 2014, : 3773 - 3778
  • [36] A two-level pipelined implementation of the IDEA cryptographic algorithm
    Salomao, SLC
    Alves, VC
    Filho, EMC
    XI BRAZILIAN SYMPOSIUM ON INTEGRATED CIRCUIT DESIGN, PROCEEDINGS, 1998, : 158 - 161
  • [37] Two-level learning algorithm for multilayer neural networks
    Liu, CS
    Tseng, CH
    TENTH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 1998, : 97 - 102
  • [38] Two-level quantizer design using genetic algorithm
    Chen, WJ
    Tai, SC
    Cheng, PJ
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 1999, E82A (02) : 403 - 406
  • [39] Two-Level Intellectual Classifier Based on the SVM Algorithm
    Demidova, Liliya
    Sokolova, Yulia
    2017 6TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO), 2017, : 463 - 466
  • [40] A Two-Level Transfer Learning Algorithm for Evolutionary Multitasking
    Ma, Xiaoliang
    Chen, Qunjian
    Yu, Yanan
    Sun, Yiwen
    Ma, Lijia
    Zhu, Zexuan
    FRONTIERS IN NEUROSCIENCE, 2020, 13