Application of Fault Detection based on Hybrid Intelligent Methods

How to collect information optimally by scheduling a limited number of sensors is one of the key technologies of safety monitoring for large mechanical equipment systems. In this paper, an improved model of Pulse Coupled Neural Network is used for the discretization algorithm of continuous features. And based on classificatory error parameter and approximate dependence degree in Variable Precision Rough Sets theory, a VPRS data model is created by utilizing the measured signals from the multi-sensor Bridge Erecting Machine Safety Monitoring System. Then the corresponding relations between time-frequency characteristics and operating condition classifications are analyzed. Finally, while the determinability of decision making analysis is enhanced, the validity order of safety monitoring information of the multi-sensor sampling points is gained, which can direct optimal scheduling of position of sensors in large-scale mechanical equipment systems.