A variety of data-driven methods have been proposed to predict remaining useful life (RUL) of key component for rolling bearings. The accuracy of data-driven RUL prediction model largely depends on the extraction method of performance degradation features. The individual heterogeneity and working condition difference of rolling bearings lead to the different performance degradation curves of rolling bearings, which result in the mismatch between the established RUL prediction model by the training rolling bearings and the test rolling bearings. If a feature is found, which can reflect the consistency of the performance degradation curve of each rolling bearings, and give the indicator to determine the node and predictable interval of the declining period, the accuracy of the RUL prediction model will be improved. To solve this problem, a new feature extraction method based on the data-driven method, namely, Fitting Curve Derivative Method of Maximum Power Spectrum Density (FDMPD), is proposed to extract the performance degradation features of the same or similar rolling bearings from the historical state monitoring data in this article. The FDMPD can make the performance degradation feature curves of life cycle, which takes on consistency trend for different rolling bearings, and the starting point of the rolling bearings to enter the degenerating period is defined and the working stage of rolling bearings is divided. Based on this, the kernel extreme learning machine (KELM) and weight application to failure times (WAFT) are combined with FDMPD to establish a new RUL prediction model of rolling bearings, which can effectively realize the RUL prediction of rolling bearings. The whole life cycle data of rolling bearings are used to verify the validity of the RUL prediction model. The experimental results show that the established RUL prediction model can accurately predict the RUL of rolling bearings. It has the advantages of rapidity, stability, and applicability.

中文翻译：

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数据驱动的滚动轴承剩余使用寿命预测的特征提取

已经提出了多种数据驱动的方法来预测滚动轴承关键部件的剩余使用寿命（RUL）。数据驱动的RUL预测模型的准确性很大程度上取决于性能下降特征的提取方法。滚动轴承的个体异质性和工作条件差异导致滚动轴承的性能下降曲线不同，从而导致由训练滚动轴承建立的RUL预测模型与测试滚动轴承之间的不匹配。如果找到可以反映每个滚动轴承性能下降曲线的一致性并提供确定下降周期的节点和可预测间隔的指标的特征，则将提高RUL预测模型的准确性。为了解决这个问题，提出了一种基于数据驱动方法的特征提取新方法，即最大功率谱密度拟合曲线微分法（FDMPD），从历史状态监测数据中提取相同或相似滚动轴承的性能下降特征。本文。FDMPD可以绘制出寿命周期的性能退化特征曲线，对不同的滚动轴承呈现一致趋势，并定义了滚动轴承进入退化期的起点，划分了滚动轴承的工作阶段。在此基础上，结合内核极限学习机（KELM）和权重应用于故障时间（WAFT），建立了新的滚动轴承RUL预测模型，可以有效地实现滚动轴承的RUL预测。滚动轴承的整个生命周期数据用于验证RUL预测模型的有效性。实验结果表明，所建立的RUL预测模型可以准确预测滚动轴承的RUL。它具有快速，稳定和适用性的优点。