基于深度学习的精密滚珠丝杠副服役性能退化过程状态评估及预测方法研究

Prognostics and health management (PHM) is an important way to improve the serviceability and maintainability of mechanical systems, which are being increasingly complex, integrated, intelligent. The development trend of high-end CNC machine tool of being high reliability and smart health management requires a high precision retaining ability of ball screw. Based on the idea of PHM, this project carries out the health monitoring, status assessment and performance prognostics of the precision degradation process of ball screw under the actual working conditions. Specifically, the vibration and temperature signals during the operation of ball screw are acquired. The empirical mode decomposition and wavelet decomposition methods are employed to extract the energy entropy, intrinsic mode function entropy, statistical features, etc. from the raw signals. The auto-associative kernel regression method is used to fuse the multiple features to reconstruct the health indicators. Three metrics are constructed to quantify the goodness of the health indictors. The optimization algorithm, multi-objective binary differential evolution is used to select the optimal health indictor, which will be used to characterize the precision degradation of ball screw. The long short-term memory neural network is proposed to assess the status of degradation process while the nonlinear autoregressive with external inputs neural network is employed to perform the prognostics of the ball screw degradation. The distribution of the remaining useful life of ball screw is predicted. The implementation of this project is expected to provide support for the smart health management of the high-end CNC machine tool.

预测及健康管理(PHM)技术是提高日趋复杂、综合化和智能化机械系统服役性和维修保障性的重要手段。本项目针对高档数控机床高可靠性及整机智能化健康保障的发展方向对精密滚珠丝杠副日益增长的高精度保持性需求，基于PHM思想开展滚珠丝杠副实际工况下精度退化过程的在线监测、状态评估及性能预测相关方法研究。采集丝杠副运行中振动和温升信号，基于经验模态分解、小波包分解等方法提取能量熵、本征模函数熵在内的多维特征，提出自相关内核回归多维重构方法对多维特征进行融合构建特征空间。设计考察特征优劣性的多维量化指标，并以上述指标为目标，基于多目标二元差分演化方法在特征空间中选择最优解，作为描述丝杠副性能退化的健康因子。设计深度学习中的长短期记忆神经网络对健康因子时序序列进行分类实现对精度退化状态的评估。设计带外部输入非线性自回归网络实现对未来精度退化水平的预测。本项目的实施有望为数控机床整机智能化健康保障提供支持。