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针对机床主轴状态直接影响加工产品精度及刀具寿命的问题,对加工过程中的主轴故障状态监测技术进行了研究,提出了一种基于虚拟仪器的旋转机械主轴故障在线监测系统。该系统以主轴振动、转速及温度为监测信号,采用了时域分析、频域分析和小波包分析相结合的信号处理技术,提取了能反映主轴运行状态的特征参数,建立了基于模糊C均值聚类算法的主轴故障识别模型,通过计算当前故障特征参数与模型中已知状态的隶属度来判断主轴状态;最后,采用了基于虚拟仪器技术的Lab VIEW软件作为编程工具,设计了一套旋转机械主轴故障在线监测与诊断系统软件,并在实际机床中进行了理论验证和实例验证。研究结果表明,该系统可在线实时识别主轴的冲击、摩擦、松动、不平衡等故障,具有速度快、效率高的特点,平均准确率达99%。
Aiming at the problem that the spindle state of the machine tool directly affects the precision of the machining products and the tool life, the spindle fault condition monitoring technology in the machining process is studied. A virtual instrument-based on-line monitoring system of the spindle faults of the rotating machine is proposed. The system takes the spindle vibration, rotation speed and temperature as the monitor signals, and adopts the signal processing technology which combines the time domain analysis, the frequency domain analysis and the wavelet packet analysis, and extracts the characteristic parameters which can reflect the operating status of the spindle. Based on the fuzzy C-mean Clustering algorithm to identify the spindle fault model, by calculating the current fault characteristic parameters and the membership of the known state model to determine the status of the spindle; Finally, the use of LabVIEW based on virtual instrument technology as a programming tool, designed a set of rotation Mechanical spindle fault on-line monitoring and diagnosis system software, and in the actual machine theory verification and case validation. The results show that the system can recognize the spindle impact, friction, loosening, unbalance and other faults on line in real time with the characteristics of high speed and high efficiency, with an average accuracy of 99%.