Sensor fault diagnosis in inland navigation networks based on a grey-box model

被引:1
|
作者
Segovia, P. [1 ,2 ,3 ]
Blesa, J. [2 ]
Duviella, E. [3 ]
Rajaoarisoa, L. [3 ]
Nejjari, F. [1 ]
Puig, V. [1 ,2 ]
机构
[1] Univ Politecn Cataluna, Res Ctr Supervis Safety & Automat Control CS2AC, Terrassa Campus,Gaia Bldg,Rambla St Nebridi 22, Terrassa 08222, Spain
[2] CSIC UPC, Inst Robot & Informat Ind, C Llorens i Artigas 4-6, Barcelona 08028, Spain
[3] Univ Lille, IMT Lille Douai, Unite Rech Informat Automat, F-59000 Lille, France
来源
IFAC PAPERSONLINE | 2018年 / 51卷 / 24期
关键词
Large-scale systems; inland waterways; fault diagnosis; grey-box model; SYSTEM; DESIGN; REACH;
D O I
10.1016/j.ifacol.2018.09.658
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Inland navigation networks are equipped with limnimeters to measure and record water level data for the control of water levels and the management of water resources. When faults occur on sensors, corrupted data can be considered as correct, leading to undesirable management actions. Therefore, it is necessary to detect and localize these faults. In this paper, the detection and localization of sensor faults is performed through the analysis of the parameters of a grey-box model, which are obtained from available real data. The parameters are determined with a sliding window, with the exception of the delays, which are considered known a priori. A fault is detected and then localized when there is a change in the value of the parameters. This approach is well suited for constant faults and particularly well adapted for intermittent faults. Data of an inland navigation reach located in the north of France are used to highlight the performance of the proposed approach. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:742 / 747
页数:6
相关论文
共 50 条
  • [1] Fault Diagnosis based on Grey-box Neural Network Identification Model
    Zhaohui, Cen
    Jiaolong, Wei
    Rui, Jiang
    INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2010), 2010, : 249 - 254
  • [2] A grey-box machine learning based model of an electrochemical gas sensor
    Aliramezani, Masoud
    Norouzi, Armin
    Koch, Charles Robert
    SENSORS AND ACTUATORS B-CHEMICAL, 2020, 321 (321):
  • [3] Grey-box Model Identification and Fault Detection of Wind Turbines Using Artificial Neural Networks
    Rahimilarki, Reihane
    Gao, Zhiwei
    2018 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2018, : 647 - 652
  • [4] Analysis of grey-box neural network-based residuals for consistency-based fault diagnosis
    Mohammadi, Arman
    Krysander, Mattias
    Jung, Daniel
    IFAC PAPERSONLINE, 2022, 55 (06): : 1 - 6
  • [5] Automated Design of Grey-Box Recurrent Neural Networks for Fault Diagnosis using Structural Models and Causal Information
    Jung, Daniel
    LEARNING FOR DYNAMICS AND CONTROL CONFERENCE, VOL 168, 2022, 168
  • [6] Model-Based Grey-Box Fuzzing of Network Protocols
    Pan, Yan
    Lin, Wei
    Jiao, Liang
    Zhu, Yuefei
    SECURITY AND COMMUNICATION NETWORKS, 2022, 2022
  • [7] Grey-box model for pipe temperature based on linear regression
    Kicsiny, Richard
    INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2017, 107 : 13 - 20
  • [8] Grey-box model for model predictive control of buildings
    Klanatsky, Peter
    Veynandt, Francois
    Heschl, Christian
    ENERGY AND BUILDINGS, 2023, 300
  • [9] A grey-box model describing the hydraulics in a creek
    Jónsdóttir, H
    Jacobsen, JL
    Madsen, H
    ENVIRONMETRICS, 2001, 12 (04) : 347 - 356
  • [10] Distributed agent-based building grey-box model identification
    Baumelt, T.
    Dostal, J.
    CONTROL ENGINEERING PRACTICE, 2020, 101