Identification of a roller screw for diagnosis of flight control actuator

Romain Breuneval, G. Clerc, B. Nahid-Mobarakeh, B. Mansouri, Alexandre Guyamier
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引用次数: 4

Abstract

The condition based maintenance is an increasing challenge for flight control systems. In this paper, a methodology for the diagnosis of a roller screw in an electromechanical actuator is proposed. As this component is critical, its diagnosis is essential to use it on aircrafts. The methodology is based on the extraction of features by identifying a model of the actuator. First, a specific waveform, made of increasing steps of speed, is run on the actuator. Then, the measurements are processed to reduce the noise and the bias of the different sensors. In order to accelerate the identification, an equivalent point is calculated for each step of the waveform. Then, the identification is realized and the identified parameters are gathered in a feature vector. Finally, a model including backlash and deformation of the stem is used to validate the approach and to generate a set of data. The aging is simulated by making assumptions on the evolution of parameters. Classification is made by using k-Nearest Neighbors (kNN). Performances of the algorithm on this application are evaluated in terms of precision and robustness.
用于飞控执行机构诊断的滚柱螺杆辨识
基于状态的维护是飞控系统面临的一个越来越大的挑战。本文提出了一种机电致动器中滚子螺杆故障的诊断方法。由于该部件至关重要,因此其诊断对于在飞机上使用至关重要。该方法是基于识别驱动器模型的特征提取。首先,在执行器上运行由速度递增步骤组成的特定波形。然后,对测量结果进行处理,以减小不同传感器的噪声和偏置。为了加快识别速度,波形的每一步都计算一个等效点。然后,实现识别,并将识别的参数集合到特征向量中。最后,利用一个包含齿隙和杆的变形的模型来验证该方法并生成一组数据。通过对参数演化的假设,模拟了老化过程。使用k近邻(kNN)进行分类。从精度和鲁棒性两方面评价了该算法在该应用中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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