Hybrid Diagnostic System Based Upon Simulation And Artificial Intelligence
CONDITION MONITORING '99, PROCEEDINGS(1999)
摘要
The authors have developed an intelligent, adaptive diagnostic hybrid system for the interpretation of vibration data for the condition monitoring of rotating machinery. The main objective was to produce a diagnostic system capable of ensuring a high level of machine reliability across a wide variety of potential plant configurations. The diagnostic hybrid system uses a variety of intelligent techniques such as neural network;, neurofuzzy logic and rule based reasoning to enable accurate and reliable predictions A machine health to be made. Such intelligent techniques have been successfully used in other competing systems, however existing systems are generally dedicated to specific items of plant and are not adaptive. In order to achieve a generic diagnostic system it was necessary to develop a simulation model capable of representing the underlying mathematical features of a basic plant configuration. The use of data from a specially constructed test rig and real-world data gathered from user sites was used to refine the initial model. As the accuracy and confidence of the simulation model increased more sophisticated configurations could be generated thus providing the data necessary for training the neural networks and neurofuzzy systems.
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biomedical sciences,chemistry
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