Residual Life Prediction of Mechanical Equipment Based on Feature Learning in Big Data Background
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摘要: 传统数据驱动剩余寿命的预测方法是通过信号处理从监测数据中手动提取特征并构建健康指标，而在大数据背景下，手动提取特征需要特定专家知识并耗费大量人力，为解决该问题，提出了一种基于特征学习的机械设备剩余寿命预测方法——自适应特征学习寿命预测方法（AFLRULP）. 该方法构建移动窗口数据矩阵解决单次采样中的数据波动问题，并建立了多层一维卷积神经网络将数据矩阵映射为机械设备的健康状态；根据失效阈值可以计算出机械设备的剩余寿命；采样轴承全寿命周期数据集合对提出的AFLRULP进行验证，并且与传统基于手动提取特征的方法进行寿命预测准确性的对比. 研究结果表明：AFLRULP不需要人工提取特征，可从原始监测数据映射为机械设备的性能状态与剩余寿命，相对于现有的基于手动提取特征的寿命预测方法，提出的方法在轴承寿命预测累积相对准确率上平均提高了0.20.Abstract: For the traditional data-driven methods of remaining useful life prediction, health indicators are generally built with some hand-crafted feature extraction methods. However, in the big data era, hand-crafted feature extraction methods require specific expert knowledge and time-costing. In order to solve this problem, a feature learning based method (adaptive feature learning based remaining useful life prediction, AFLRULP) was proposed to predict remaining useful life of the mechanical equipment. Firstly, a window data matrix was constructed to solve the data fluctuation problem. Then, a one-dimensional convolutional neural network with multi-layers was built to map the data matrix to the health conditions of the mechanical equipment. Finally, the remaining useful life was predicted with a failure threshold. In order to verify the effectiveness of the proposed method, it is validated by a bearing life-cycle dataset. Additionally, the proposed method was compared with some manual feature extraction based methods. The results show that the proposed method (AFLRULP) does not need to manually extract features, it can map the original monitoring data to the performance status and remaining life of mechanical equipment. Additionally, compared with the existing life prediction methods based on manual feature extraction, the proposed method improves the cumulative relative accuracy of bearing life prediction by an average of 0.20.
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图 1 自适应特征学习寿命预测方法
Figure 1. Adaptive feature learning based remaining useful life prediction
图 3 不同方法所预测的剩余寿命
Figure 3. Residual life of bearing 1 and bearing 2 predicted by different methods
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