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摘要
Multimedia technologies are increasingly used in design of CNC grinding machines. The self-diagnosis system is a necessary part. To make use of multimedia and solve the problem about the lack of diagnosis knowledge included in the self-diagnosis module used in CNC grinding machines, this paper addresses the fault diagnosis knowledge discovery and the decision support model for fault diagnosis designed in a CNC grinding machine based on on-line vibration monitoring. The fault diagnosis characteristics of CNC grinding machine are analyzed first. Then the rough set theory is introduced to reduce the redundant information affecting fault diagnosis, thereby discover the fault diagnosis knowledge in the form of rules. Further, this paper proposes a decision support model of fault diagnosis module for modern CNC grinding machine, based on the theory advantages of redundant information reduction supported by rough set, being easy to find the diagnosis knowledge in the maintenance and fault diagnosis records. The model is applied into an engineering example of fault diagnosis for a given type of grinding machine. Useful knowledge of fault diagnosis is discovered with the engineering interpretation of fault diagnosis process. The performance comparison with other typical diagnosis methods verifies the validity and advantage of the proposed decision support model based on rough set theory. This paper provides a multimedia solution integrated with rough set theory and information technology for diagnosis module developed in modern CNC grinding machines, and can be a theoretical and technical reference to build fault diagnosis systems used in other modern machines.
- 出版日期2017-10
- 单位同济大学
