In order to effectively analyze the failure factors that affect the precision of glass machining and verify the methods and systems for failure prediction, establish a flexible, open and reconfigurable database and knowledge base environment for the evaluation of the performance of the glass machinery and the analysis of the failure mechanism. Provide conditions for data and knowledge of fault forecasting. In order to effectively use the established database and knowledge base, and further use data mining theory, extract or'mine' useful information and knowledge from a large number of sample data of the operating state of glass machinery, research and propose a dynamic performance sample of the whole machine based on the principle of data mining The method of data database construction to realize the effective organization and utilization of the whole machine sample database and knowledge base. In addition, the actual operation of the glass machinery is based on the test of sample data collection and database construction of typical functional components of the whole machine. In the test, data collection of test-cut standard specimens of various materials and data collection and analysis of simulated typical failures are carried out. On the basis of experimental research, the method of obtaining sample data and building a database is revised and optimized, and the failure prediction test of typical functional components is carried out to realize the data acquisition and analysis of test samples. Article finishing: glass edging machine
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