Adaptive fault detection and diagnosis using parsimonious Gaussian mixture models trained with distributed computing techniques

DOI10.1016/j.jfranklin.2016.11.024zbMath1398.93324OpenAlexW2559541339MaRDI QIDQ1796632

Reinaldo M. Palhares, Carlos H. de M. Bomfim, Walmir M. Caminhas, Mário Cesar M. M. de Campos, Ubirajara Fumega, Benjamin R. Menezes, Thiago A. Nakamura, André P. Lemos

Publication date: 17 October 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2016.11.024

zbMATH Keywords

Gaussian mixture modelsadaptive fault detectionprocess automation

Mathematics Subject Classification ID

Related Items (4)

Multiscale Gaussian convolution algorithm for estimate of Gaussian mixture model ⋮ Robust fault hiding approach for T-S fuzzy systems with unmeasured premise variables ⋮ Conditional random field for monitoring multimode processes with stochastic perturbations ⋮ Approximate solution to optimal linear quadratic Gaussian control over non-acknowledgment networks

Uses Software

• MapReduce
• Mixmod
• Hadoop
• Spark

Cites Work

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• Control Procedures for Residuals Associated with Principal Component Analysis
• Probabilistic Principal Component Analysis
• Efficient Greedy Learning of Gaussian Mixture Models
• The Generalization of Student's Ratio
• A survey on concept drift adaptation
• Some Computer Organizations and Their Effectiveness
• Fault detection and diagnosis in industrial systems

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