Abstract Roll-to-roll systems that include rotary components such as driven rolls and idle rollers have significant potential for application in fabrication of flexible functional devices. They are inexpensive, mass producible, and environmentally friendly; however, even minor defects in their component bearings can render them susceptible to severe damage, which necessitates accurate diagnoses of bearing quality. The main steps in machine learning for fault diagnosis include feature extraction and selection. In the case of high-dimensional feature data, critical study is required to identify the best feature combination for proper diagnosis. Thus, this study aims to develop a method that extracts fault characteristics of a bearing from the measured signal and qualify the bearing according to the Mahalanobis distances and differences in density between normal and faulty data groups. Features extracted from vibration data collected from industry-scale roll-to-roll systems and CWRU data were trained with principal component analysis, other modern feature selection techniques, and the proposed algorithm-based eight classifiers. Compared with the existing algorithm, the accuracy increased by up to 9.24%, the training time decreased by up to 34.46%, and the number of features to obtain the maximum accuracy decreased by up to 59.92%. Thus, the proposed algorithm provides an effective and time-efficient approach to improve the accuracy of fault diagnosis of rotary components.

中文翻译：

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基于密度和距离的特征选择算法应用于卷对卷制造系统的故障诊断

摘要 包括从动辊和惰辊等旋转部件的卷对卷系统在制造柔性功能器件方面具有巨大的应用潜力。它们价格低廉、可大量生产且对环境友好；然而，即使是部件轴承中的微小缺陷也会使它们容易受到严重损坏，这需要对轴承质量进行准确诊断。用于故障诊断的机器学习的主要步骤包括特征提取和选择。在高维特征数据的情况下，需要进行批判性研究以确定最佳特征组合以进行正确诊断。因此，本研究旨在开发一种从测量信号中提取轴承故障特征的方法，并根据马氏距离和正常和故障数据组之间的密度差异对轴承进行鉴定。从工业规模的卷对卷系统收集的振动数据和 CWRU 数据中提取的特征通过主成分分析、其他现代特征选择技术和所提出的基于算法的八分类器进行训练。与现有算法相比，准确率提升高达 9.24%，训练时间减少高达 34.46%，获得最大准确率的特征数量减少高达 59.92%。因此，所提出的算法为提高旋转部件故障诊断的准确性提供了一种有效且省时的方法。