Fuzzy set approach to signal detection

Automated supervision and fault diagnosis are important features in design of efficient and reliable systems. Detection algorithms are generally optimised with respect to a particular set of cost functions chosen for the specific application. In the last few years in the field of detection systems there have been an increasing number of applications based on algorithms using methodologies, which belong to a subclass of Artificial Intelligence called Soft Computing. In this paper, we propose a fuzzy method for the detection of dangerous states based on matching a predefined database of these states with periodically measured or estimated parameter values.