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Paper details
Number 2 - June 2013
Volume 23 - 2013
Residual generator fuzzy identification for automotive diesel engine fault diagnosis
Silvio Simani
Abstract
Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system
failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include
both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing
units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should
be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this
problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these
residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design
of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the
model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy.
The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.
Keywords
fault detection and isolation, analytical redundancy, Takagi–Sugeno fuzzy prototypes, residual generator fuzzy modelling and identification, real diesel engine