图结构联合时序数据驱动的装备剩余使用寿命预测方法

doi:10.3901/JME.2023.12.183

机械工程学报 ›› 2023, Vol. 59 ›› Issue (12): 183-194.doi: 10.3901/JME.2023.12.183

• 特邀专栏：制造大数据分析与决策 • 上一篇下一篇

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图结构联合时序数据驱动的装备剩余使用寿命预测方法

沈天浩^1,2, 丁康¹, 黎杰³, 黄如意^2,4, 李巍华^1,2,4

1. 华南理工大学机械与汽车工程学院广州 510641;
2. 人工智能与数字经济广东省试验室(广州) 广州 510335;
3. 广州华工机动车检测技术有限公司广州 510730;
4. 华南理工大学吴贤铭智能工程学院广州 511442

收稿日期:2022-09-30 修回日期:2023-01-30 出版日期:2023-06-20 发布日期:2023-08-15
通讯作者: 黎杰(通信作者),男,1964年出生,教授级高级工程师。主要研究方向为汽车检测与控制、故障预测与预测性维护。E-mail:106lj@163.com;李巍华(通信作者),男,1973年出生,博士,教授,博士研究生导师。主要研究方向为工业智能、工业大数据、装备智能运维、汽车智能驾驶。E-mail:whlee@scut.edu.cn
作者简介:沈天浩,男,1998年出生。主要研究方向为故障预测与预测性维护、基于深度学习的设备剩余使用寿命预测。E-mail:202020101019@mail.scut.edu.cn
基金资助:
国家重点研发计划(2018YFB1702400)、国家自然科学基金(52275111,52205100)、广东省基础与应用基础研究基金自然科学基金面上(2023A1515012856)和中国博士后科学基金(2022M711197)资助项目。

Graph Structure and Temporal Data Driven Remaining Useful Life Prediction Method for Machinery

SHEN Tianhao^1,2, DING Kang¹, LI Jie³, HUANG Ruyi^2,4, LI Weihua^1,2,4

1. School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641;
2. Guangdong Artificial Intelligent and Digital Economy Laboratory (Guangzhou), Guangzhou 510335;
3. Guangzhou Huagong Automobile Inspection Technology Co., LTD., Guangzhou 510730;
4. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442

Received:2022-09-30 Revised:2023-01-30 Online:2023-06-20 Published:2023-08-15

摘要/Abstract

摘要： 随着制造业向数字化、智能化方向转型升级，基于数据驱动的装备智能运维成为了学术界和工业界研究的潮流。然而，当前剩余寿命预测方法存在时序信息提取能力弱、难以建立监测数据与装备真实退化趋势的准确映射关系等局限性。为解决上述问题，提出一种图结构联合时序数据驱动的装备剩余使用寿命预测方法。首先，融合图卷积网络的图时序信息表征能力与长短时记忆网络的长时序特征刻画能力，构建包含图卷积、长短时记忆和逻辑回归模块的剩余寿命预测模型；其次，利用特征相关性构造的具有时序特性的图结构数据、利用原始数据构造具有固定时间步长的时序数据，分别作为图卷积模块和长短时记忆模块的输入，以最小化预测损失为目标，训练并优化寿命预测模型；最后，利用轴承全寿命加速退化试验数据，验证所提方法的有效性，所提方法在单工况下RMSE为0.107，综合工况下RMSE为0.099。与领域内先进方法对比的试验结果表明，所提方法取得了最优的预测性能，可为装备的预测与健康管理提供决策依据，具有较强的工程应用价值。

关键词: 剩余寿命预测, 图神经网络, 长短时记忆网络, 滚动轴承, 装备智能运维

Abstract: With the digital and intelligent transformation in manufacturing industry, the data-driven intelligent maintenance for machinery has been attracted more and more attention from both the academic and industrial society in recent years. However, the existing remaining life prediction methods have some limitations, such as the poor performance for extracting useful time sequence information and for establishing accurate mapping between monitoring data and real degradation trend. To solve the above problems, a graph structure and temporal data driven remaining useful life prediction method is proposed for machinery. First, the remaining life prediction model is constructed by combining the graph convolution neural network(GCN) and the long short-term memory (LSTM). Second, the original data are used to construct the fixed time-step temporal data, and are also used to construct the graph structure data by using the feature correlation algorithm. The graph data and temporal data is used as the input of GCN and LSTM module, respectively. Furthermore, the life prediction model can be trained and optimized by minimizing the prediction loss. Finally, the performance of the proposed method is verified based on an accelerated bearing degradation dataset. The RMSE of the proposed method is 0.107 in a single working condition and 0.099 in a comprehensive working condition. The experimental results show that the proposed method achieves a better performance than that of the other state-of-the-art methods and provides a promising solution for prognostic and health management of machinery in practical industrial application.

Key words: remaining useful life prediction, GNN, LSTM, rolling bearing, intelligent operation and maintenance

中图分类号:

TH206
TP183

沈天浩, 丁康, 黎杰, 黄如意, 李巍华. 图结构联合时序数据驱动的装备剩余使用寿命预测方法[J]. 机械工程学报, 2023, 59(12): 183-194.

SHEN Tianhao, DING Kang, LI Jie, HUANG Ruyi, LI Weihua. Graph Structure and Temporal Data Driven Remaining Useful Life Prediction Method for Machinery[J]. Journal of Mechanical Engineering, 2023, 59(12): 183-194.

参考文献

[1] 张洁,汪俊亮,吕佑龙,等. 大数据驱动的智能制造[J]. 中国机械工程,2019,30(2):127-133,158. ZHANG Jie,WANG Junliang,LÜ Youlong,et al. Big data driven intelligent manufacturing[J]. China Mechanical Engineering,2019,30(2):127-133,158.
[2] LI Jialin,LI Xueyi,DAVID H. A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction[J]. IEEE Access,2019,7:75464-75475.
[3] WEN Long,LI Xinyu,GAO Liang,et al. A new convolutional neural network-based data-driven fault diagnosis method[J]. IEEE Transactions on Industrial Electronics,2017,65(7):5990-5998.
[4] 庞哲楠,裴洪,李天梅,等. 考虑不完美维修的随机退化设备剩余寿命自适应预测方法[J]. 机械工程学报,2023,59(2):14-29. PANG Zhenan,PEI Hong,LI Tianmei,et al. Remaining useful lifetime prognostic approach for stochastic degradation equipment considering imperfect maintenance activities[J]. Journal of Mechanical Engineering,2023,59(2):14-29.
[5] 陈祝云,钟琪,黄如意,等. 基于增强迁移卷积神经网络的机械智能故障诊断[J]. 机械工程学报,2021,57(21):96-105. CHEN Zhuyun,ZHONG Qi,HUANG Ruyi,et al. Intelligent fault diagnosis for machinery based on enhanced transfer convolutional neural network[J]. Journal of Mechanical Engineering,2021,57(21):96-105.
[6] LEI Yaguo,LI Naipeng,GUO Liang,et al. Machinery health prognostics:A systematic review from data acquisition to RUL prediction[J]. Mechanical systems and signal processing,2018,104:799-834.
[7] International Organization for Standardization. ISO 13381-1. Condition Monitoring and Diagnostics of Machines-Prognostics-Part 1:General Guidelines[S]. Geneva,Switzerland:ISO,2015.
[8] LEI Yaguo,LI Naipeng,GONTARZ S,et al. A model-based method for remaining useful life prediction of machinery[J]. IEEE Transactions on reliability,2016,65(3):1314-1326.
[9] 王久健,杨绍普,刘永强,等. 一种基于空间卷积长短时记忆神经网络的轴承剩余寿命预测方法[J]. 机械工程学报,2021,57(21):88-95. WANG Jiujian,YANG Shaopu,LIU Yongqiang,et al. A method of bearing remaining useful life estimation based on convolutional long short-term memory neural network[J]. Chinese Journal of Mechanical Engineering,2021,57(21):88-95.
[10] 李乃鹏,蔡潇,雷亚国,等. 一种融合多传感器数据的数模联动机械剩余寿命预测方法[J]. 机械工程学报,2021,57(20):29-37,46. LI Naipeng,CAI Xiao,LEI Yaguo,et al. A model-data-fusion remaining useful life prediction method with multi-sensor fusion for machinery[J]. Journal of Mechanical Engineering,2021,57(20):29-37,46.
[11] AGGAB T,VRIGNAT P,AVILA M,et al. Remaining useful life estimation based on the joint use of an observer and a hidden Markov model[J]. Proceedings of the Institution of Mechanical Engineers,Part O:Journal of Risk and Reliability,2021:236(5):676-695.
[12] WANG Biao,LEI Yaguo,LI Naipeng,et al. Deep separable convolutional network for remaining useful life prediction of machinery[J]. Mechanical Systems and Signal Processing,2019,134:106330.
[13] 车畅畅,王华伟,倪晓梅,等. 基于1D-CNN和Bi-LSTM的航空发动机剩余寿命预测[J]. 机械工程学报,2021,57(14):304-312. CHE Changchang,WANG Huawei,NI Xiaomei,et al. Residual life prediction of aeroengine based on 1D-CNN and Bi-LSTM[J]. Journal of Mechanical Engineering,2021,57(14):304-312.
[14] LIU Junqiang,LEI Fan,PAN Chunlu,et al. Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion[J]. Reliability Engineering & System Safety,2021,214:107807.
[15] 舒涛,张一弛,丁日显. 基于灰色模型与LSTM网络的旋转机械轴承寿命预测[J]. 系统工程与电子技术,2021,43(8):2355-2361. SHU Tao,ZHANG Yichi,DING Rixian. Life prediction of bearings in rotating machinery based on grey model and LSTM network[J]. Systems Engineering and Electronics,2021,43(8):2355-2361.
[16] MONTI F,BOSCAINI D,MASCI J,et al. Geometric deep learning on graphs and manifolds using mixture model cnns[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. New York:IEEE,2017:5115-5124.
[17] HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural computation,1997,9(8):1735-1780.
[18] 黄如意,李霁蒲,王震,等. 基于多任务学习的装备智能诊断与寿命预测方法[J]. 中国科学:技术科学,2022,52(1):123-137. HUANG Ruyi,LI Jipu,WANG Zhen,et al. Intelligent diagnostic and prognostic method based on multitask learning for industrial equipment[J]. Sci. Sin. Tech.,2022,52(1):123-137.
[19] 雷亚国,韩天宇,王彪,等. XJTU-SY滚动轴承加速寿命试验数据集解读[J]. 机械工程学报,2019,55(16):1-6. LEI Yaguo,HAN Tianyu,WANG Biao,et al. XJTU-SY rolling element bearing accelerated life test datasets:A tutorial[J]. Journal of Mechanical Engineering,2019,55(16):1-6.
[20] GU Yingxin,WYLIE B,BOYTE S,et al. An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data[J]. Remote Sensing,2016,8(11):943.
[21] KINGMA D,Ba J. Adam:A method for stochastic optimization[C]//International Conference on Learning Representations,ICLR 2015,San Diego,California,May 7-9,2015.
[22] ZHANG Wei,PENG Gaoliang,LI Chuanhao,et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J]. Sensors,2017,17(2):425.
[23] ZHENG Shuai,RISTOVSKI K,FARAHAT A,et al. Long short-term memory network for remaining useful life estimation[C]//2017 IEEE international conference on prognostics and health management (ICPHM). IEEE,2017:88-95.
[24] HINCHI A,TKIOUAT M. Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network[J]. Procedia Computer Science,2018,127:123-132.
[25] CHEN Yuxuan,JIN Yi,JIRI G. Predicting tool wear with multi-sensor data using deep belief networks[J]. The International Journal of Advanced Manufacturing Technology,2018,99(5):1917-1926.
[26] SHE Daoming,JIA Minping. A BiGRU method for remaining useful life prediction of machinery[J]. Measurement,2021,167:108277.
[27] ZHU Xingquan,WU Xindong. Class noise vs. attribute noise:A quantitative study[J]. Artificial Intelligence Review,2004,22(3):177-210.

图结构联合时序数据驱动的装备剩余使用寿命预测方法

Graph Structure and Temporal Data Driven Remaining Useful Life Prediction Method for Machinery

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