Ball screw electro-mechanical actuators are commonly found in high precision motion control applications including aerospace systems as well as automated setups for industries. These actuators perform flight / application critical job and ball screw drives are responsible to provide precise linear motion while carrying thrust loading. A failure in ball screw drive may disturb positioning accuracy of overall system. At present, few techniques are available to monitor electro-mechanical actuators for aerospace and industrial systems. This paper provides a deep learning based intelligent technique to monitor condition of ball screw actuators. The proposed scheme utilizes modified residual learning scheme to extract features from two-dimensional transformed motor current signals. The current signal data was collected under different load domains in terms of magnitude and direction reversal. A 2D-Remanant-CNN (2D-Rem-CNN) model was developed for features extraction with proposed optimized softmax for classification of mechanical faults. The proposed technique was validated against different ball screw fault cases. The testing results prove the superiority of 2D-Rem-CNN model against different state of the art techniques. The proposed framework was also tested for system’s stability under different load domains.

中文翻译：

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一种具有优化 Softmax 的新型基于二维电流信号的残差学习，用于识别滚珠丝杠执行器中的故障

滚珠丝杠机电执行器常见于高精度运动控制应用中，包括航空航天系统以及工业自动化设置。这些执行器执行飞行/应用关键工作，滚珠丝杠驱动负责在承载推力载荷的同时提供精确的线性运动。滚珠丝杠驱动故障可能会影响整个系统的定位精度。目前，很少有技术可用于监控航空航天和工业系统的机电执行器。本文提供了一种基于深度学习的智能技术来监控滚珠丝杠执行器的状态。所提出的方案利用改进的残差学习方案从二维变换后的电机电流信号中提取特征。电流信号数据是在不同载荷域下根据幅度和方向反转收集的。开发了 2D-Remanant-CNN (2D-Rem-CNN) 模型用于特征提取，并提出了用于机械故障分类的优化 softmax。所提出的技术针对不同的滚珠丝杠故障情况进行了验证。测试结果证明了 2D-Rem-CNN 模型对不同最先进技术的优越性。还测试了所提出的框架在不同负载域下的系统稳定性。测试结果证明了 2D-Rem-CNN 模型对不同最先进技术的优越性。还测试了所提出的框架在不同负载域下的系统稳定性。测试结果证明了 2D-Rem-CNN 模型对不同最先进技术的优越性。还测试了所提出的框架在不同负载域下的系统稳定性。