摘要
滚珠丝杠的性能是影响数控机床加工精度的重要因素之一。提出一种机床滚珠丝杠的性能衰退及健康状态的评估方法,该方法结合拉普拉斯特征降维与马氏距离分析模型,建立不同健康状态下传感器信号样本点在特征空间中与健康值的非线性映射关系,从而得到滚珠丝杠性能衰退程度的量化评估。通过不同健康状态的滚珠丝杠性能试验,将该方法应用于滚珠丝杠的驱动电动机速度和转矩信号,通过对传感器信号的内蕴流形及其不同健康状态下的采样特征点在内蕴特征空间中分布相关性分析,以得到量化的性能评估结果。与常见方法所得结果相比,该模型能准确地反映滚珠丝杠的性能衰退趋势,鲁棒性更好。该方法可采用数控机床自带的传感器,无须改动机床整体结构,不影响其动态加工性能,可广泛应用于工业数控机床滚珠丝杠的在线实时健康状态评估。
The performance of ball screw is one of the important factors which affect the CNC machining precision. A method to assess the performance degradation and health status of ball screws is proposed based on the combination of Laplacian eigenmaps and Mahalanobis distance analysis to establish a nonlinear mapping relationship between the characteristics of the signals and health status of the ball screw. The health values are calculated to represent the degree of the performance degradation. Experiments have been conducted by testing the ball screws of different health status. The proposed method is performed on the speed and torque signals from the drive motor for validation. Compared with the results from the traditional dimensionality reduction methods, the results show that the proposed model is more accurate and robust. The model is featured with both the correlation analysis from the Mahalanobis distance analysis and the manifold learning analysis from the method of Laplacian eigenmaps. In addition, the method can be performed on the build-in sensors of the CNC machine tools so that there is no need to change the original structure design to avoid to the potential interferences with the dynamic processing performance, which enables a wide industrial applications of the online real-time health status assessment for the ball screws of the CNC machine tools.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2017年第15期125-130,共6页
Journal of Mechanical Engineering
基金
国家自然科学基金(51305258)
国家科技支撑计划(2014BAD08B01)
国家科技重大专项(2014ZX04015-021)资助项目
关键词
滚珠丝杠
性能衰退
健康评估
拉普拉斯特征值映射
马氏距离
ball screw
performance degradation
health evaluation
Laplacian eigenmaps
Mahalanobis distance
