Subspace-based fault detection robust to changes in the noise covariances-Reference-Cited by-同舟云学术

Subspace-based fault detection robust to changes in the noise covariances

Published:2013-09 Issue:9 Volume:49 Page:2734-2743
ISSN:0005-1098
Container-title:Automatica
language:en
Short-container-title:Automatica

Author:

Döhler Michael,Mevel Laurent

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Reference54 articles.

1. Basawa, I. V. (1985). Neyman–Le Cam tests based on estimating functions. In Proceedings of Berkeley conference in honor of Jerzy Neyman and Jack Kiefer, Vol. 2 (pp. 811–825).

2. On-board component fault detection and isolation using the statistical local approach;Basseville;Automatica,1998

3. Model-based statistical signal processing for change and damage detection;Basseville,2009

4. Subspace-based fault detection algorithms for vibration monitoring;Basseville;Automatica,2000

5. Output-only subspace-based structural identification: from theory to industrial testing practice;Basseville;Journal of Dynamic Systems, Measurement, and Control,2001

Cited by 43 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Statistical Subspace-Based Damage Detection and Jerk Energy Acceleration for Robust Structural Health Monitoring;Buildings;2023-06-27

2. Fine-grained transfer learning based on deep feature decomposition for rotating equipment fault diagnosis;Measurement Science and Technology;2023-03-21

3. Comparative Study of Meta-heuristic Algorithms for Damage Detection Problem;Computational Science – ICCS 2023;2023

4. A Segmental Autoencoder-based Fault Detection for Nonlinear Dynamic Systems: An Interpretable Learning Framework;2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS);2022-08-03

5. Fault Detection for Systems With Model Uncertainty and Disturbance via Coprime Factorization and Gap Metric;IEEE Transactions on Cybernetics;2022-08