Global-and-local-structure-based neural network for fault detection

DOI10.1016/j.neunet.2019.05.022zbMath1441.62162OpenAlexW2952598456WikidataQ92954948 ScholiaQ92954948MaRDI QIDQ2185622

Yudong Chen, Zhihui Lai, Hai-Tao Zhao

Publication date: 5 June 2020

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2019.05.022

zbMATH Keywords

fault detectionprincipal component analysisdimension reductionfeedforward neural networkstatistical process monitoring

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics in engineering and industry; control charts (62P30) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (3)

Effective dimensionality reduction using kernel locality preserving partial least squares discriminant analysis ⋮ Multivariate statistical process monitoring based on principal discriminative component analysis ⋮ GLSNN

Uses Software

• GitHub

Cites Work

• Unnamed Item
• Orthogonal Procrustes rotation for two or more matrices
• A survey of the statistical theory of shape. With discussion and a reply by the author
• A data-driven approach to actuator and sensor fault detection, isolation and estimation in discrete-time linear systems
• Inter-class sparsity based discriminative least square regression
• Recursive transformed component statistical analysis for incipient fault detection
• Batch process monitoring with tensor factorization
• A Multivariate Exponentially Weighted Moving Average Control Chart
• Fault detection and diagnosis in industrial systems

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