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A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings

机译：一种基于极端学习机的两级方法，用于预测滚动元件轴承剩余使用寿命的基础

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摘要

Rolling-element bearing is one of the main parts of rotating equipment. In order to avoid the mechanical equipment damage caused by the sudden failure of rolling-element bearings, it is necessary to monitor the condition of bearing and predict its life. Therefore, a two-stage prediction method based on extreme learning machine is proposed to predict the remaining useful life of rolling-element bearings quickly and accurately. This method uses the relative root mean square value (RRMS) to divide the operation stage of the bearing into two stages: normal operation and degradation. Starting from the normal operation stage, according to the principle of univariate prediction, a feedback extreme learning machine model is constructed for real-time short-term prediction of bearing degradation trend. Once the predicted value shows that the bearing has entered the degradation stage, the sensitive features are selected as the input by correlation analysis, and the multi variable feedback extreme learning machine model, which takes into account the dual advantages of multivariable regression and small sample prediction, is constructed to predict the remaining useful life. The experimental results show that the proposed method has higher short-term prediction accuracy and faster operation speed in the case of limited learning sample size.
机译：滚动元件轴承是旋转设备的主要部分之一。为了避免由滚动元件轴承突然发生故障引起的机械设备损坏，有必要监测轴承的条件并预测其生命。因此，提出了一种基于极端学习机的两级预测方法，以便快速准确地预测滚动元件轴承的剩余使用寿命。该方法使用相对根均方值（RRMS）将轴承的操作阶段划分为两个阶段：正常操作和劣化。从正常操作阶段开始，根据单变量预测原理，构建了反馈极端学习机模型，用于轴承降解趋势的实时短期预测。一旦预测值表明轴承已进入劣化阶段，选择敏感特征作为相关分析的输入，以及多变量反馈极限学习机模型，这考虑了多变量回归和小样本预测的双重优势建造，以预测剩余的使用寿命。实验结果表明，在有限的学习样本大小的情况下，该方法具有更高的短期预测精度和更快的操作速度。

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