论文标题
跟踪和可视化降解的迹象，以预测滚动轴承的早期失败预测
Tracking and Visualizing Signs of Degradation for an Early Failure Prediction of a Rolling Bearing
论文作者
论文摘要
预测性维护，即预测失败是工业4.0的支柱之一。一种有效的方法是在发生故障之前跟踪早期退化的迹象。本文为机器提供了创新的故障预测方案。所提出的方案结合了由机器和数据可视化技术引起的全部振动数据的使用。该方案无需培训数据，并在安装后快速开始。首先，我们建议使用全光谱（作为高维数据矢量），没有裁剪，也没有复杂的特征提取，并通过将高维矢量映射到2D地图中来可视化数据行为。然后，我们可以确保流程的简单性，并且忽略重要信息的可能性较小，并提供人类友好且可理解的产出。其次，我们提出了实时数据跟踪器（RTDT），该数据跟踪器（RTDT）通过在正常数据组成的2D地图上绘制目标机器的实时频率频谱数据来预测适当时间的失败，并通过足够的时间进行维护。第三，我们使用由公共数据集IMS数据集提供的现实世界测试对失败测量的轴承的振动数据显示了提案的测试结果。
Predictive maintenance, i.e. predicting failure to be few steps ahead of the fault, is one of the pillars of Industry 4.0. An effective method for that is to track early signs of degradation before a failure happens. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of full spectrum of the vibration data caused by the machines and data visualization technologies. This scheme is featured by no training data required and by quick start after installation. First, we propose to use full spectrum (as high-dimensional data vector) with no cropping and no complex feature extraction and to visualize data behavior by mapping the high dimensional vectors into a 2D map. We then can ensure the simplicity of process and less possibility of overlooking of important information as well as providing a human-friendly and human-understandable output. Second, we propose Real-Time Data Tracker (RTDT) which predicts the failure at an appropriate time with sufficient time for maintenance by plotting real-time frequency spectrum data of the target machine on the 2D map composed from normal data. Third, we show the test results of our proposal using vibration data of bearings from real-world test-to-failure measurements provided by the public dataset, the IMS dataset.
