Neural networks and statistical decision making for fault diagnosis of PM linear synchronous machines

DOI10.1080/00207721.2020.1792579zbMath1483.93454OpenAlexW3047313557MaRDI QIDQ5026823

Gerasimos G. Rigatos, V. Siadimas, Nicholas Zervos, Masoud Abbaszadeh, Dimitrios N. Serpanos, Pierluigi Siano

Publication date: 8 February 2022

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2020.1792579

zbMATH Keywords

\(\chi^2\) distributionGauss-Hermite neural networkspermanent magnet linear synchronous machinesresiduals sequencestatistical fault diagnosis

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Applications of statistics in engineering and industry; control charts (62P30) Application models in control theory (93C95)

Cites Work

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• Fuzzy model validation using the local statistical approach
• Fault detection and isolation in nonlinear dynamic systems: A combined input-output and local approach
• Early warning of slight changes in systems
• Adaptive Modelling, Estimation and Fusion from Data
• Neural Structures Using the Eigenstates of a Quantum Harmonic Oscillator
• The asymptotic local approach to change detection and model validation

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