A novel dynamic non-Gaussian approach for quality-related fault diagnosis with application to the hot strip mill process

DOI10.1016/J.JFRANKLIN.2016.10.029zbMath1355.93216OpenAlexW2544577786MaRDI QIDQ509342

Yong-Cai Geng, Sumit K. Garg

Publication date: 9 February 2017

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2016.10.029

zbMATH Keywords

independent component analysis (ICA)non-Gaussianhot strip mill process (HSMP)partial least squares (PLS)-based methodsprocess industriesquality-related fault diagnosis problem

Mathematics Subject Classification ID

Reliability, availability, maintenance, inspection in operations research (90B25) Production models (90B30) Least squares and related methods for stochastic control systems (93E24)

Related Items (5)

Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes ⋮ A novel collaborative diagnosis approach of incipient faults based on VMD and SCN for rolling bearing ⋮ Improved key performance indicator-partial least squares method for nonlinear process fault detection based on just-in-time learning ⋮ KPI relevant and irrelevant fault monitoring with neighborhood component analysis and two-level PLS ⋮ A key performance indicator-based fault detection scheme for marine diesel turbocharging systems

Uses Software

• FastICA

Cites Work

• Cooperative control of a nonuniform gantry crane with constrained tension
• Model-based fault diagnosis techniques. Design schemes, algorithms and tools
• Geometric properties of partial least squares for process monitoring
• Finite-horizon \(H_{\infty }\) fault estimation for linear discrete time-varying systems with delayed measurements
• Fault detection and diagnosis in industrial systems

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