Authors:
Juan C. Tudon Martinez
;
Ruben Morales-Menendez
and
Luis E. Garza Castañón
Affiliation:
Tecnológico de Monterrey, Mexico
Keyword(s):
Dynamic principal component analysis, Diagnostic observers, Fault detection, Fault diagnosis.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
Abstract:
A comparison between the Dynamic Principal Component Analysis (DPCA) method and a bank of Diagnostic Observers (DO) under the same experimental data from a shell and tube industrial heat exchanger is presented. The comparative analysis shows the performance of both methods when sensors and/or actuators fail. Different metrics are discussed (i.e. robustness, quick detection, isolability capacity, explanation facility, false alarm rates and multiple faults identifiability). DO showed quicker detection for sensor and actuator faults with lower false alarm rate. Also, DO can isolate multiple faults. DPCA required a minor training effort; however, it cannot identify two or more sequential faults.