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Intelligent System Monitoring: On-Line Learning and System Condition State
Abstract
A general methodology for intelligent system monitoring is proposed in this chapter. The methodology combines degradation hybrid automata to system degradation tracking and a nonlinear adaptive model for model-based diagnosis and prognosis purposes. The principal idea behind this approach is monitoring the plant for any off-nominal system behavior due a wear or degradation. The system degradation is divided in subspaces, from fully functional, nominal, or faultless mode to no functionality mode, failure. The degradation hybrid automata, uses a nonlinear adaptive model for continuous flow dynamics and a system condition guard to transition between modes. Error Filtering On- line Learning (EFOL) scheme is introduced to design a parametric model and adaptive low in such a way that the unknown part of the adaptive model function is on-line approximated; the on-line approximation is via a Radial Basis Function Neural Network (RBFNN). To validate the proposed methodology, a complete conveyor belt simulator, based on a real system, is designed on Simulink; the degradation is characterized using the Paris-Erdogan crack growth function. Once the simulator is designed the measured current, i’s, and velocity of the IM, ?m, are used to modeling the simplified adaptive IM model. EFOL scheme is used to on-line approximate the unknown TL function. The simplified adaptive model estimates the IM velocity, , as output. and the measured IM velocity ?m are compared to detect any deviation from the nominal system behavior. When the degradation automata detect a system condition change the adaptive model on-line approximate the new TL.
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