This paper describes a pattern recognition (PR) technique, which uses learning vector quantization (LVQ). This method is adapted for practical application to solve problems in the area of condition monitoring and fault diagnosis where a number of fault signatures are involved. In these situations, the aim is health monitoring, including identification of deterioration of the healthy condition and identification of causes of the failure in real-time. For this reason a fault database is developed which contains the collected information about various states of operation of the system in the form of pattern vectors. The task of the real-time monitoring system is to correlate patterns of unknown faults with the known fault signatures in the fault database. This will determine cause of failure and degree of deterioration of the system under test. The problem of fault diagnosis may involve a large number of patterns and large sampling time, which affects the learning stage of neural networks. The study here also aims to find a fast learning model of neural networks for instances when a high number of patterns and numerous processing elements are involved. It begins searching for an appropriate solution. The study is extended to the enforcement learning models and considers LVQ as a network emerged from the competitive learning model through enforcement training. Finally, tests show an accuracy of 92.3 per cent in the fault diagnostic capability of the technique.
The impact of the COVID-19 epidemic on primary care in South-eastern France : implementation of a real-time monitoring system based on regional health insurance system data
