A data-driven fault detection approach for unknown large-scale systems based on GA-SVM

DOI10.1016/j.ins.2023.120023MaRDI QIDQ6153222

Zhenlei Ma, Jie Sun, Xiao-Jian Li

Publication date: 13 February 2024

Published in: Information Sciences (Search for Journal in Brave)

zbMATH Keywords

support vector machine (SVM)data-drivenfault detection (FD)

Mathematics Subject Classification ID

Large-scale systems (93A15)

Cites Work

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• Adaptive fuzzy decentralized tracking fault-tolerant control for stochastic nonlinear large-scale systems with unmodeled dynamics
• Data-driven fault estimation of non-minimum phase LTI systems
• A data-driven approach to actuator and sensor fault detection, isolation and estimation in discrete-time linear systems
• Fault detection filter design for data reconstruction-based continuous-time networked control systems
• Guaranteed model-based fault detection in cyber-physical systems: a model invalidation approach
• Echo state network with multiple delayed outputs for multiple delayed time series prediction
• Data-driven fault detection for large-scale network systems: a mixed optimization approach
• Subspace identification of individual systems in a large-scale heterogeneous network
• A Distributed Networked Approach for Fault Detection of Large-Scale Systems
• Filtering and System Identification
• Fault Estimation Filter Design With Guaranteed Stability Using Markov Parameters
• Decentralized control design for large-scale systems with strong interconnections using neural networks

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