Time-varying fault diagnosis for asynchronous multisensor systems based on augmented IMM and strong tracking filtering
type: Item
instance of: scholarly article
title: Time-varying fault diagnosis for asynchronous multisensor systems based on augmented IMM and strong tracking filtering
zbMATH Open document ID: 1403.93182
DOI: 10.1155/2018/5205698
author: Yanyan Hu
author: Xiaoling Xue
author: Zengwang Jin
author: Kaixiang Peng
published in: Journal of Control Science and Engineering
publication date: 2018-12-12T00:00:00Z
review text: Summary: A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive MultiModel (IMM) fusion filtering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered and a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of possible fault mode. By doing this, the dilemma of predetermining the fault extent as model design parameters in traditional IMM-based approaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track abrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance of fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility and effectiveness of the proposed method.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93E10
zbMATH DE Number: 6992150
zbMATH Keywords: interactive multimodel fusion filtering
zbMATH Keywords: strong tracking filtering
zbMATH Keywords: multisensors dynamic systems
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/5205698
OpenAlex ID: W2782416729
cites work: Fault diagnosis and compensation for two‐dimensional discrete time systems with sensor faults and time‐varying delays
cites work: Intelligent vehicle embedded sensors fault detection and isolation using analytical redundancy and nonlinear transformations
cites work: Unbiased Minimum Variance Fault and State Estimation for Linear Discrete Time-Varying Two-Dimensional Systems
cites work: The interacting multiple model algorithm for systems with Markovian switching coefficients
cites work: Sensor placement for fault location identification in water networks: a minimum test cover approach
description: scientific article; zbMATH DE number 6992150