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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LEI Y, LI N, GUO L, et al. Machinery health prognostics:a systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104: 799-834. doi: 10.1016/j.ymssp.2017.11.016 司小胜, 胡昌华. 数据驱动的设备剩余寿命预测理论及应用[M]. 北京: 国防工业出版社, 2016. LEI Y. Intelligent fault diagnosis and remaining useful life prediction of rotating machinery[M]. Oxford: Elsevier Butterworth-Heinemann, 2016: 102-108. 陈雪峰, 訾艳阳. 智能运维与健康管理[M]. 北京: 机械工业出版社, 2018: 25-28. GUO L, LEI Y, XING S, et al. Deep convolutional transfer learning network:a new method for intelligent fault diagnosis of machines with unlabeled data[J]. IEEE Transactions on Industrial Electronics, 2019, 66(9): 7316-7325. doi: 10.1109/TIE.2018.2877090 GUO L, LEI Y, LI N, et al. Machinery health indicator construction based on convolutional neural networks considering trend burr[J]. Neurocomputing, 2018, 292: 142-150. doi: 10.1016/j.neucom.2018.02.083 裴洪,胡昌华,司小胜,等. 基于机器学习的设备剩余寿命预测方法综述[J]. 机械工程学报,2019,55(8): 1-8. doi: 10.3901/JME.2019.08.001FEI Hong, HU Changhua, SI Xiaosheng, el al. Review of machine learning based remaining useful life prediction methods for equipment[J]. Journal of Mechanical Engineering, 2019, 55(8): 1-8. doi: 10.3901/JME.2019.08.001 XIN W L, ZHEN H W, YU D F. Remaining life predictions of fan based on time series analysis and BP neural networks[C]//2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference. [S.l.]: IEEE, 2016: 607-611. HUANG C G, HUANG H Z, PENG W W, et al. Improved trajectory similarity-based approach for turbofan engine prognostics[J]. Journal of Mechanical Science and Technology, 2019, 33(10): 1-14. WANG B, LEI Y, LI N, et al. A hybrid prognostics approach for estimating remaining useful Life of rolling element bearings[J]. IEEE Transactions on Reliability, 2020, 69(1): 401-402. doi: 10.1109/TR.2018.2882682 BYINGTON M. WATSON D E. Data-driven neural network methodology to remaining life predictions for aircraft actuator components[C]//2004 IEEE Aerospace Conference Proceedings. [S.l.]: IEEE, 2004: 3581-3589. HUANG C G, HUANG H Z, LI Y F. A bidirectional LSTM prognostics method under multiple operational conditions[J]. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8792-8802. doi: 10.1109/TIE.2019.2891463 GEBRAEEL N, LAWLEY M, LIU R. Residual life predictions from vibration-based degradation signals:a neural network approach[J]. IEEE Transactions on industrial electronics, 2004, 51(3): 694-700. doi: 10.1109/TIE.2004.824875 高宏力,李登万,许明恒. 基于人工智能的丝杠寿命预测技术[J]. 西南交通大学学报,2010,45(5): 685-691.GAO Hongli, LI Dengwan, XU Mingheng. Intelligent monitoring system for screw life evaluation[J]. Journal of Southwest Jiaotong University, 2010, 45(5): 685-691. TIAN Z. An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring[J]. Journal of Intelligent Manufacturing, 2012, 23(2): 227-237. doi: 10.1007/s10845-009-0356-9 GUO L, LI N, JIA F, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings[J]. Neurocomputing, 2017, 240: 98-109. doi: 10.1016/j.neucom.2017.02.045 HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647 HE K, ZHANG X, REN S, ET AL. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [S.l.]: IEEE Computer Society, 2016: 770-778. HINTON G, DENG L, YU D, et al. Deep neural networks for acoustic modeling in speech recognition[J]. IEEE Signal Processing Magazine, 2012, 29(6): 82. doi: 10.1109/MSP.2012.2205597 YAN H, WAN J, ZHANG C, et al. Industrial big data analytics for prediction of remaining useful life based on deep learning[J]. IEEE Access, 2018, 6: 17190-17197. doi: 10.1109/ACCESS.2018.2809681 REN L, SUN Y, WANG H, et al. Prediction of bearing remaining useful life with deep convolution neural network[J]. IEEE Access, 2018, 6: 13041-13049. doi: 10.1109/ACCESS.2018.2804930 -
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