Residual Life Prediction of Mechanical Equipment Based on Feature Learning in Big Data Background
-
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.
-
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
-
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 期刊类型引用(16)
1. 王天予，陈雪静，梁修惠. 基于振动信号变化的轨道交通设备状态监测系统. 自动化与仪器仪表. 2024(01): 7-11 . 
百度学术2. 程尧，陈丙炎，张卫华，李夫忠. 基于加权联合提升包络谱的轴箱轴承故障诊断. 西南交通大学学报. 2024(01): 142-150 . 
本站查看3. 和征，李忠鹏，杨小红. 基于数字孪生与k-近邻算法的车间设备运行状态预测研究. 制造技术与机床. 2024(03): 193-199 . 百度学术4. 谢晓丽，姚兴平. 基于PSO-DBN结构的不平衡大数据分类研究. 长沙大学学报. 2024(02): 15-22+50 . 百度学术5. 李亚杰，李昭楠. 基于微服务架构的设备管理系统设计. 机床与液压. 2024(11): 125-131 . 百度学术6. 金义，宋永春，马路遥，曹雨，宋树，贺琳. 高维时序空间大数据关联特征精准挖掘算法. 电子设计工程. 2024(16): 161-165 . 百度学术7. 郝悦，王晶，孙成德，袁文彬，程伟. 基于大数据的储罐大修周期预测方法. 石油化工腐蚀与防护. 2024(05): 32-38 . 百度学术8. 谌卓玲，卢绍文，张亚军，潘庆玉. 工业过程指标的平滑交替辨识预报算法. 控制理论与应用. 2024(09): 1539-1547 . 百度学术9. 贾俊青，李磊，吕超，刘文杰，郑大鹏，李赫. 基于电流谐波的电力电缆状态诊断和寿命分析方法. 内蒙古电力技术. 2023(03): 72-77 . 百度学术10. 张阳，谢立华，段正强. 基于ASP模式的机械设备多特征参数健康状态综合评价. 机械设计与制造工程. 2023(08): 112-116 . 百度学术11. 张远亮，李海浪. 基于PVC-CAE的轴承剩余寿命预测方法. 计算机应用研究. 2023(10): 3070-3075 . 百度学术12. 秦春林，石建刚，任帅. 基于大数据分析的轴承退卸工装智能化研究. 自动化技术与应用. 2023(11): 60-63 . 百度学术13. 贾志涛，王萌，任鹏飞，田淋元. 基于门控循环单元的涡扇发动机剩余寿命预测. 工业技术与职业教育. 2022(02): 22-24+87 . 百度学术14. 璩晶磊，马晓杰，梁萍. 基于BAS-BP模型的轴承剩余使用寿命预测. 机床与液压. 2022(18): 172-175 . 百度学术15. 赵思恒，周航，周少伟. 基于维纳过程的船舶柴油机增压器寿命预测. 中国舰船研究. 2022(06): 126-132 . 百度学术16. 刘致远，刘渊博，杨峰，鲁凯旋，刘妤. Spark平台下基于优化BP神经网络模型的接触疲劳剩余寿命预测. 重庆理工大学学报(自然科学). 2021(10): 111-119 . 百度学术其他类型引用(11)
-
下载:
百度学术
点击查看大图
图(4)
计量
- 文章访问数: 728
- HTML全文浏览量: 630
- PDF下载量: 120
- 被引次数: 27