Multi-sensor Fusion Approach with Fault Detection and Isolation Based on two-state Probability Ratio

被引:0
作者
Qiang, Xingzi [1 ]
Xue, Rui [1 ]
Zhu, Yanbo [1 ]
机构
[1] Beihang Univ, Sch Elect & Informat Engn, Beijing, Peoples R China
来源
2021 SIXTH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET) | 2021年
基金
中国国家自然科学基金;
关键词
fault detection; two-state probability ratio; fault isolation; nonlinear filter;
D O I
10.1109/WISPNET51692.2021.9419448
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Aiming at the problem of fault detection in the process of multi-sensor data fusion, a two-state probability ratio fault detection and isolation method is proposed in this paper, which can realize the independent detection of single sensor fault. Firstly, the state space of the current state under a certain confidence condition is selected according to the process equation, and then the probability of occurrence of the likelihood distribution of each sensor in this space is calculated, which is regarded as a test statistic. The threshold of the test statistics is designed according to the false alarm probability requirement of the system. The data fusion will be performed after isolating the sensor, when the test statistic is less than the threshold. The simulation shows that this method can detect the faults of each sensor independently in a multi-sensor system.
引用
收藏
页码:325 / 329
页数:5
相关论文
共 11 条
[1]   Multi-sensor fusion approach with fault detection and exclusion based on the Kullback-Leibler Divergence: Application on collaborative multi-robot system [J].
Al Hage, Joelle ;
El Najjar, Maan E. ;
Pomorski, Denis .
INFORMATION FUSION, 2017, 37 :61-76
[2]  
[Anonymous], 2011, SENSOR FAULT DETECTI
[3]  
Chen R. H., 2002, INTELLIGENT TRANSPOR
[4]   Analytic hierarchy process for multi-sensor data fusion based on belief function theory [J].
Frikha, Ahmed ;
Moalla, Hela .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2015, 241 (01) :133-147
[5]   Fault detection in satellite power system using convolutional neural network [J].
Ganesan, M. ;
Lavanya, R. ;
Nirmala Devi, M. .
TELECOMMUNICATION SYSTEMS, 2021, 76 (04) :505-511
[6]   PROCESS FAULT-DETECTION BASED ON MODELING AND ESTIMATION METHODS - A SURVEY [J].
ISERMANN, R .
AUTOMATICA, 1984, 20 (04) :387-404
[7]  
Jiang C., 2020, J GEODESY GEOINFORMA, V3, P14
[8]   A method for judicious fusion of inconsistent multiple sensor data [J].
Kumar, Manish ;
Garg, Devendra P. ;
Zachery, Randy A. .
IEEE SENSORS JOURNAL, 2007, 7 (5-6) :723-733
[9]  
Qiang X., IEEE T SIGNAL PROCES
[10]  
Rahmani A., 2010, INT C MECH EL ENG