Construction of Fault Knowledge Base for Monitoring High-End Turning Center

High-end turning center is one of the main production equipment in the modern manufacturing industry. In order to effectively guarantee the reliable, stable and safe operation, test research was carried out to build the knowledge base of fault diagnosis of machine tools. Orientating to the needs of monitoring typical functional components of high-end turning center, the test platform was built and the model of fault knowledge base was constructed. The wavelet packet theory was used to extract fault energy feature and the knowledge acquisition technology based on rough sets theory was employed. Test results indicate that the synthesized method of wavelet analysis and rough sets could acquire the fault rules of CNC machine tools effectively, improve the fault diagnosis rate and provide reliable data to predict the fault of machine tools. A key test technology to analyse the factors leading to failures is also presented in this research.