高档车削中心故障知识库构建技术

面向高档车削中心典型功能部件,构建状态监测试验平台,提出机床故障知识库模型;采用小波包理论进行故障能量特征提取;研究基于粗糙集理论的知识获取技术.实验结果表明,小波包分析与粗糙集方法相结合能够有效获取机床故障规则,提高了故障诊断率,为实现机床故障预测提供可靠数据来源,为分析导致故障的影响因素提供了关键试验技术.

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.