The Development Of CNC Machine Condition Intelligent Monitoring System

With the rapid development of automobile industry, the growing demand for high speed, high precision machining flexible CNC machine, we used conventional CNC machines, the linear axis (such as X axis) maximum linear feed rate has reached 90 meters/minutes, the scale resolution is achieved 0.1um,High speed, high precision, flexibility, these indexes will continue to lead the future development of CNC machine.For the powertrain plant, CNC equipment accounted for more than 90% of all the machine. Therefore, in the actual production and operation part, for the machine management, it is one of the biggest challenges is to improve machine reliability, reduce operating costs, lean operation index of CNC system.Based on the above requirements, using network technology, massive amounts of data generated in the running process of the machine, to be captured, using expert diagnosis model, read these data, to predict the failure,improve the reliability of machine, reduce operation cost, the pre diagnosis model using the concept of industrial 4.0, called:machine condition intelligent monitoring system.The project is mainly study on the following three aspects:1). Based network,servo data high frequency sampling technique (10 ms/cycle), to build the front data acquisition system, software design, innovative using FANUC open FOCAS dynamic link database, control FANUC servo viewer software, to realize automatic, high frequency sampling system.2). Research of the storage and management of big data. To build a data management framework platform, using the open source Historian database, to filter and store the data.3). Establish a diagnosis model, make the interpretation of abstract data, and machine failure. We used 200 sets of high speed and high precision CNC machines as the research object, database capacity has reached about 24 months. Based on these massive data of machine, training the diagnosis model, the prediction accuracy continues to increase.Based on the above research, the system has reduced down time of 9.18%(2014 and 2015 May). At the same time, in the servo energy consumption reduction, the production efficiency improvement, also have many successful applications...