Research On Computer Intelligent Monitoring And Controling Used For Machining Center

With the development of automation and the high demand of reliableness, Machining Center has got important status and predomination in manufacturing. Machining Center has grown as key and deciding factor in many plants. Without timely fault diagnosis and service, serious economic loss can be caused. Rough Sets theory has made fast progress in recent years, it has outstanding ability in research of expressing, learning , concluding non-precise knowledge. It is based on practical large data sets, and deduces, find the knowledge and key of the classification systems.So this paper studies a method of Machining Center fault diagnosis based on Rough Sets theory, which is one of the latest tools in Data Mining area. Not like the usual methods that based on mechanical vibrancy, this method combines the Rough Sets theory with the Artificial Neural Network.The practicality of using rough set to reduce the date was discussed, in this paper, and the interval-valued continuous attribute discretization by applying self-organizing map neural network clustering was proposed, too. This article proposes a normal concision's method, which reduces the example's condition attribute and eliminates the redundant information of the date. What's more, it provides the methods used to diagnosis Machining Center's faults based on the intelligence hybrid system by adopting the MATLAB neural network workbox. At last, the functional modules which make up into intelligence hybrid system for fault diagnosis was introduced, including: data acquisition module, date preprocessor module, date reduction module, neural network module and fault diagnosis module. Herein, intelligence hybrid system based on rough sets and neural network for fault diagnosis is established in this paper.