HIGH-SPEED RT MONITORING SYSTEM USING NEURAL NETWORKS

摘要

This paper describes a high-speed reconfigurable neural networks for monitoring operational status of automated machinery. Continuous operation of precision machines may change their system performance due to wear, deterioration, or failure. A Learning Vector Quantization (LVQ) based technique is developed that is capable of monitoring system status accurately, and updating its knowledge base with new heuristic data. This method is adapted for practical application to solve problems of condition monitoring and fault diagnosis where a number of fault signatures are initially available. In these situations, the aim is health monitoring, including identification of deterioration of the healthy condition and identification of causes of the failures. A hard real-time system is designed and implemented. An early-warning system monitors sensitive parameter, of pressure and current sensors. Their variations beyond a defined healthy threshold trigger a nondestructive testing, which produces transient signals. Correlating the transient pattern of a fault with a database of known failures determines the severity and degree of deterioration of the system. Vigorous tests on real machines indicated an accuracy of 92.3% for the LVQ based monitoring system.