Identification of fuzzy relational models for fault detection

Abstract

This paper presents the concept of fuzzy relational models for use in a fuzzy output estimator. A suitable "eld of application is in fault diagnosis, where output observation rather than state observation is needed for the generation of fault re#ecting residual signals. Due to their non-linear structure, fuzzy relational models can be used appropriately for building models of non-linear dynamic systems. In this paper, the identi"cation of fuzzy models for residual generation is discussed. Emphasis is placed upon the model-building procedure including the identi"cation of the model structure and of the parameters. As an application example, a real technical system is considered. The case study presents the detection of oversteering of a passenger car. The results of the application to residual generation are discussed.

Robust Fuzzy Fault Detection and Isolation Approach Applied to Surge in Centrifugal Compressor Modeling and Control

“…Fuzzy models are flexible mathematical structures that, in analogy to nonlinear models, have been recognized as universal function approximators [1,3,23]. Fuzzy models use 'If-Then' rules and logical connectives to establish relations between the variables defined for the model of the system.…”

“…These crisp values are fuzzified and processed using the fuzzy knowledge base [1,3,20,23]. The fuzzy output is defuzzified in throttle and the CCV gains in order to control the plants operating conditions.…”

Fuzzy modeling and control for detection and isolation of surge in industrial centrifugal compressors

“…However, Takagi-Sugeno fuzzy systems are capable of serving as the analytical model for nonlinear systems due to its universal approximation property, that is, any desired approximation accuracy can be achieved by increasing the size of the approximation structure and properly defining the parameters of the approximator [3,26]. A Takagi-Sugeno fuzzy system can be defined by:…”

“…As was introduced in [3], by applying a Takagi- Sugeno-type fuzzy model with interval parameters, one is able to approximate the upper and lower boundaries of the domain of functions that result from an uncertain system. The fuzzy model is therefore intended for robust modeling purposes; on the other hand, studies show it can be used in fault detection as well in [4 -7].…”

Estimating the Size of Incipient Faults in HVAC Equipment

“…This type of black-box model has a number of advantages: it is flexible 153 enough to approximate any nonlinear function to a certain accuracy (Brown and Harris 1994) and it has a simple structure and is linear in its adjustable parameters so that a computationally undemanding parameter estimation scheme, such as recursive least-squares, can be used to identify the model on-line (Amann et al 2001).…”