A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks
A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks
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DOI：
10.1109/tii.2018.2868687
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发表时间：
2019-06-01
影响因子：
12.3
通讯作者：
Wang, Zhongren
中科院分区：
文献类型：
--
作者：
Xia, Min;Li, Teng;Wang, Zhongren
轴承的退化在工业机械的故障中起着关键作用。轴承的预测对于采用最佳维护策略至关重要，通过估计轴承的剩余使用寿命（RUL）来降低总体成本并避免不必要的停机时间甚至人员伤亡。传统的数据驱动的RUL预测方法在很大程度上依赖于使用人类专业知识的手动特征提取和选择。本文提出了一种创新的两阶段自动化方法，使用深度神经网络（DNN）来估计轴承的RUL。基于去噪自动编码器的DNN用于将监测轴承的采集信号分类到不同的退化阶段。通过训练DNN直接从原始信号中提取代表性特征。然后，基于浅层神经网络的回归模型构建的每个健康阶段。通过平滑来自不同模型的回归结果来获得最终的RUL结果。该方法对不同工况下的真实的轴承退化数据集取得了令人满意的预测性能。
The degradation of bearings plays a key role in the failures of industrial machinery. Prognosis of bearings is critical in adopting an optimal maintenance strategy to reduce the overall cost and to avoid unwanted downtime or even casualties by estimating the remaining useful life (RUL) of the bearings. Traditional data-driven approaches of RUL prediction rely heavily on manual feature extraction and selection using human expertise. This paper presents an innovative two-stage automated approach to estimate the RUL of bearings using deep neural networks (DNNs). A denoising autoencoder-based DNN is used to classify the acquired signals of the monitored bearings into different degradation stages. Representative features are extracted directly from the raw signal by training the DNN. Then, regression models based on shallow neural networks are constructed for each health stage. The final RUL result is obtained by smoothing the regression results from different models. The proposed approach has achieved satisfactory prediction performance for a real bearing degradation dataset with different working conditions.