Fuzzy logic-based decision-making for fault diagnosis in a DC motor

Authors:
Luis J. de Miguel;L. Felipe Blázquez
Affiliations:
Department of Systems Engineering and Control, University of Valladolid, E.T.S.I.I., Paseo del Cauce s/n, 47011 Valladolid, Spain;Department of Electronic and Electrical Engineering, University of León, E.II.I.I., Campus de Vegazana s/n, 24071 León, Spain
Venue:
Engineering Applications of Artificial Intelligence
Year:
2005

Citing 8
Cited 5

System identification: theory for the user

System identification: theory for the user
A new structural framework for parity equation-based failure detection and isolation

Automatica (Journal of IFAC)
Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results

Automatica (Journal of IFAC)
Detection of abrupt changes: theory and application

Detection of abrupt changes: theory and application
Fault diagnosis of machines via parameter estimation and knowledge processing: tutorial paper

Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Neuro-fuzzy systems for diagnosis

Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Robust model-based fault diagnosis for dynamic systems

Robust model-based fault diagnosis for dynamic systems
Robust Estimation and Failure Detection: A Concise Treatment

Robust Estimation and Failure Detection: A Concise Treatment

Online fault detection and isolation of nonlinear systems based on neurofuzzy networks

Engineering Applications of Artificial Intelligence
Recent Literature

Fuzzy Sets and Systems
Fault tolerant measurement system based on Takagi--Sugeno fuzzy models for a gas turbine in a combined cycle power plant

Fuzzy Sets and Systems
Planning for mechatronics systems-Architecture, methods and case study

Engineering Applications of Artificial Intelligence
Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The uncertainty of system models, the presence of noise and the stochastic behaviour of several variables reduce the reliability and robustness of the fault diagnosis methods. To tackle these kinds of problems, this paper presents a decision-making module based on fuzzy logic for model-based fault diagnosis applications. Fuzzy rules use the concept of fault possibility and knowledge of the sensitivities of the residual equations. A fault detection and isolation system, based on the input-output linear model parity equations approach, and including this decision-making module, has been successfully applied in laboratory equipment, resulting in a reduction of the uncertainty due to disturbances and modelling errors. Furthermore, the experimental sensitivity values of the residual equations allow the fault size to be estimated with sufficient accuracy.