为了解决机床远程故障诊断中有线网络布线干涉、设备成本高等问题,同时满足大数据存储、多样化数据接入的需求,提出基于物联网(The internet of things,IoT)无线网络和IoT云平台的故障诊断系统。系统模型设计为4层:采集层、传输层、运...为了解决机床远程故障诊断中有线网络布线干涉、设备成本高等问题,同时满足大数据存储、多样化数据接入的需求,提出基于物联网(The internet of things,IoT)无线网络和IoT云平台的故障诊断系统。系统模型设计为4层:采集层、传输层、运算层和应用层。采集层采用基于应用过程的对象连接与嵌入(Object linking and embedding for process control,OPC)和多传感器融合的数据采集方法,获得故障诊断所需数据;传输层基于窄带物联网(Narrow band internet of things,NB-IoT)无线通信技术和IoT云平台,实现数据远程传输、通信和存储;运算层基于BP神经网络在前、专家系统在后的串行反馈控制机制,建立故障诊断算法模型。以机床的主轴伺服系统为实例,分析其故障现象并获得故障样本,对诊断算法模型进行误差仿真分析,预测结果与期望相吻合,验证了该模型的有效性。展开更多
Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to ...Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to be divided into several groups on an appropriate threshold. Currently, group threshold value is mainly determined by researchers experience. Few studies focus on group threshold in temperature variable grouping. Since the threshold is important in error compensation, this paper arms to find out an optimal threshold to realize temperature variable optimization in thermal error modeling. Firstly, correlation coefficient is used to express membership grade of temperature variables, and the theory of fuzzy transitive closure is applied to obtain relational matrix of temperature variables. Concepts as compact degree and separable degree are introduced. Then evaluation model of temperature variable clustering is built. The optimal threshold and the best temperature variable clustering can be obtained by setting the maximum value of evaluation model as the objective. Finally, correlation coefficients between temperature variables and thermal error are calculated in order to find out optimum temperature variables for thermal error modeling. An experiment is conducted on a precise horizontal machining center. In experiment, three displacement sensors are used to measure spindle thermal error and twenty-nine temperature sensors are utilized to detect the machining center temperature. Experimental result shows that the new method of temperature variable optimization on optimal threshold successfully worked out a best threshold value interval and chose seven temperature variables from twenty-nine temperature measuring points. The model residual of z direction is within 3 μm. Obviously, the proposed new variable optimization method has simple computing process and good modeling accuracy, which is quite fit for thermal error compensation.展开更多
文摘为了解决机床远程故障诊断中有线网络布线干涉、设备成本高等问题,同时满足大数据存储、多样化数据接入的需求,提出基于物联网(The internet of things,IoT)无线网络和IoT云平台的故障诊断系统。系统模型设计为4层:采集层、传输层、运算层和应用层。采集层采用基于应用过程的对象连接与嵌入(Object linking and embedding for process control,OPC)和多传感器融合的数据采集方法,获得故障诊断所需数据;传输层基于窄带物联网(Narrow band internet of things,NB-IoT)无线通信技术和IoT云平台,实现数据远程传输、通信和存储;运算层基于BP神经网络在前、专家系统在后的串行反馈控制机制,建立故障诊断算法模型。以机床的主轴伺服系统为实例,分析其故障现象并获得故障样本,对诊断算法模型进行误差仿真分析,预测结果与期望相吻合,验证了该模型的有效性。
基金supported by Jiangsu Provincial Prospective Joint Research Foundation for Industry-University-Research of China (Grant No. BY2009102)Henan Provincial Major Scientific and Technological Projects of China (Grant No. 102102210050)
文摘Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to be divided into several groups on an appropriate threshold. Currently, group threshold value is mainly determined by researchers experience. Few studies focus on group threshold in temperature variable grouping. Since the threshold is important in error compensation, this paper arms to find out an optimal threshold to realize temperature variable optimization in thermal error modeling. Firstly, correlation coefficient is used to express membership grade of temperature variables, and the theory of fuzzy transitive closure is applied to obtain relational matrix of temperature variables. Concepts as compact degree and separable degree are introduced. Then evaluation model of temperature variable clustering is built. The optimal threshold and the best temperature variable clustering can be obtained by setting the maximum value of evaluation model as the objective. Finally, correlation coefficients between temperature variables and thermal error are calculated in order to find out optimum temperature variables for thermal error modeling. An experiment is conducted on a precise horizontal machining center. In experiment, three displacement sensors are used to measure spindle thermal error and twenty-nine temperature sensors are utilized to detect the machining center temperature. Experimental result shows that the new method of temperature variable optimization on optimal threshold successfully worked out a best threshold value interval and chose seven temperature variables from twenty-nine temperature measuring points. The model residual of z direction is within 3 μm. Obviously, the proposed new variable optimization method has simple computing process and good modeling accuracy, which is quite fit for thermal error compensation.
