Fuzzy fault isolation using gradient information and quality criteria from system identification models

DOI10.1016/J.INS.2015.04.008zbMath1417.93116OpenAlexW2047090954MaRDI QIDQ1749818

Markus Pichler, Kurt Pichler, Thomas Buchegger, Hajrudin Efendic, Edwin Lughofer, Francisco Serdio

Publication date: 17 May 2018

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

Full work available at URL: https://doi.org/10.1016/j.ins.2015.04.008

zbMATH Keywords

system identificationfault isolationgradient informationfuzzy fault likelihoodisolation indicatorviolation degree

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Fuzzy control/observation systems (93C42) System identification (93B30)

Related Items (3)

Robust Data-Driven Fault Detection in Dynamic Process Environments Using Discrete Event Systems ⋮ Descriptor sliding mode approach for fault/noise reconstruction and fault-tolerant control of nonlinear uncertain systems ⋮ Simultaneous fault diagnosis for robot manipulators with actuator and sensor faults

Uses Software

• ElemStatLearn
• SparseFIS

Cites Work

• Evolving fuzzy systems -- methodologies, advanced concepts and applications.
• Reconstruction-based contribution for process monitoring
• Geometric properties of partial least squares for process monitoring
• Principal component analysis.
• Fault-Diagnosis Applications
• Knowledge Discovery from Data Streams
• ON-LINE FAULT DETECTION WITH DATA-DRIVEN EVOLVING FUZZY MODELS
• Fuzzy identification of systems and its applications to modeling and control
• Fault detection and diagnosis in industrial systems
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