XJTU-SY滚动轴承加速寿命试验数据集解读

doi:10.3901/JME.2019.16.001

机械工程学报 ›› 2019, Vol. 55 ›› Issue (16): 1-6.doi: 10.3901/JME.2019.16.001

• 仪器科学与技术 • 下一篇

XJTU-SY滚动轴承加速寿命试验数据集解读

雷亚国¹, 韩天宇¹, 王彪¹, 李乃鹏¹, 闫涛¹, 杨军²

1. 西安交通大学现代设计及转子轴承系统教育部重点实验室西安 710049;
2. 浙江长兴昇阳科技有限公司湖州 313100

收稿日期:2019-04-26 修回日期:2019-07-03 出版日期:2019-08-20 发布日期:2019-08-20
通讯作者: 雷亚国(通信作者),男,1979年出生,博士,教授,博士研究生导师。主要研究方向为大数据智能故障诊断与寿命预测、机械设备健康监测与智能维护、机械系统建模与动态信号处理。E-mail:yaguolei@mail.xjtu.edu.cn
基金资助:
国家自然科学基金（61673311）和NSFC-浙江两化融合联合基金（U1709208）资助项目。

XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial

LEI Yaguo¹, HAN Tianyu¹, WANG Biao¹, LI Naipeng¹, YAN Tao¹, YANG Jun²

1. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049;
2. Zhejiang Changxing Sumyoung Technology Co., Ltd., Huzhou 313100

Received:2019-04-26 Revised:2019-07-03 Online:2019-08-20 Published:2019-08-20

摘要/Abstract

摘要： 预测与健康管理对保障机械装备安全服役、提高生产效率、增加经济效益至关重要。高质量的全寿命周期数据是预测与健康管理领域的基础性资源，这些数据承载着反映装备服役性能完整退化过程与规律的关键信息。然而，由于数据获取成本高、存储与传输技术有待发展等原因，典型的全寿命周期数据极其匮乏，严重制约了机械装备预测与健康管理技术的理论研究与工程应用。为解决上述难题，西安交通大学机械工程学院雷亚国教授团队联合浙江长兴昇阳科技有限公司，选取工业场景中典型的关键部件——滚动轴承为试验对象，开展了历时两年的滚动轴承加速寿命试验，并将获取的试验数据——XJTU-SY滚动轴承加速寿命试验数据集面向全球学者公开发布。该数据集共包含3种工况下15个滚动轴承的全寿命周期振动信号，采样频率高、数据量大、失效类型丰富、记录信息详细，既可为预测与健康管理领域提供新鲜的"数据血液"，推动故障诊断与剩余寿命预测等领域的算法研究，又可助力工业界智能化运维的"落地生根"。

关键词: 滚动轴承, 加速寿命试验, 预测与健康管理

Abstract: Prognostics and health management (PHM) is crucial for ensuring the safe operation of machinery, improving the productivity and increasing economic benefits. High-quality life-cycle data, as the basic resource in the field of PHM, are able to carry the key information which reflects the complete degradation processes of machinery. However, due to the high costs in data acquisition and insufficient development in storage and transmission technology, typical life-cycle data is extremely scarce, which limits the theoretical research and engineering application of PHM for machinery. In order to solve this dilemma, accelerated life tests of rolling element bearings are carried out by Prof. Yaguo Lei's research group from School of Mechanical Engineering, Xi'an Jiaotong University (XJTU) and the Changxing Sumyoung Technology Co., Ltd. (SY), Zhejiang. These tests lasted for two years and the acquired datasets, i.e., XJTU-SY bearing datasets, have been publicly released for all PHM researchers. The XJTU-SY bearing datasets contain run-to-failure vibration signals of 15 rolling element bearings under three different operating conditions. These datasets have high sampling frequency, large amount of data, abundant failure types and detailed recording information. Accordingly, these datasets not only provide fresh "data blood" for PHM and promote the research of fault diagnosis and remaining useful life prediction, but also are able to help to improve intelligent maintenance decision making in industry.

Key words: accelerated life tests, prognostics and health management, rolling element bearings

中图分类号:

TH133

雷亚国, 韩天宇, 王彪, 李乃鹏, 闫涛, 杨军. 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, LI Naipeng, YAN Tao, YANG Jun. XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial[J]. Journal of Mechanical Engineering, 2019, 55(16): 1-6.

参考文献

[1] 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.
[2] 何正嘉,曹宏瑞,訾艳阳,等. 机械设备运行可靠性评估的发展与思考[J]. 机械工程学报,2014,50(2):171-186. HE Zhengjia,CAO Hongrui,ZI Yanyang,et al. Developments and thoughts on operational reliability assessment of mechanical equipment[J]. Journal of Mechanical Engineering,2014,50(2):171-186.
[3] 雷亚国,贾峰,孔德同,等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报,2018,54(5):94-104. LEI Yaguo,JIA Feng,KONG Detong,et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering,2018,54(5):94-104.
[4] SAXENA A,GEOBEL K,SIMON D,et al. Damage propagation modeling for aircraft engine run-to-failure simulation[C]//International Conference on Prognostics and Health Management. IEEE,2008:1-9.
[5] QIU H,LEE J,LIN Jing,et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration,2006,289(4-5):1066-1090.
[6] NECTOUX P,GOURIVEAU R,MEDJAHER K,et al. PRONOSTIA:An experimental platform for bearings accelerated degradation tests[C]//IEEE International Conference on Prognostics & Health Management. IEEE,2012.
[7] LI X,LIM B S,ZHOU J H,et al. Fuzzy neural network modelling for tool wear estimation in dry milling operation[C]//Annual Conference of the Prognostics and Health Management Society. 2009:1-11.
[8] RAI A,UPADHYAY S H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J]. Tribology International,2016,96:289-306.
[9] WANG Biao,LEI Yaguo,LI Naipeng,et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE Transactions on Reliability,2018:1-12.
[10] LI Xiang,ZHANG Wei,DING Qian. Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction[J]. Reliability Engineering & System Safety,2019,182:208-218.
[11] ISO B S. 281:2007. Rolling bearings-Dynamaic load ratings and rating life[S]. 2nd ed. London:BS ISO,2007.
[12] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. GB/T 6391-2010滚动轴承额定动载荷和额定寿命[S]. 北京:中国标准出版社,2010. General Administration of Quality Supervision,Inspection and Quarantine of the People's Republic of China,Standardization Administration of the People's Republic of China. GB/T 6391-2010 rolling bearings-dynamic load ratings and rating life[S]. Beijing:Standards Press of China,2010.
[13] LEI Yaguo,HE Zhengjia,ZI Yanyang,et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs[J]. Mechanical systems and signal processing,2007,21(5):2280-2294.
[14] G.PROAKIS J,G.MANOLAKIS D. PROAKIS J G. Digital signal processing:Principles,algorithms and applications[M]. New York:Pearson Education Inc,2001.

[1]	崔玲丽, 王鑫, 王华庆, 胥永刚, 张建宇. 基于改进开关卡尔曼滤波的轴承故障特征提取方法[J]. 机械工程学报, 2019, 55(7): 44-51.
[2]	刘杨, 石拓, 辛喜成, 马亚新, 薛曾元, 明帅帅. 转子挤压油膜阻尼器减振效率的理论研究[J]. 机械工程学报, 2019, 55(3): 90-98.
[3]	侯磊, 陈予恕. 非线性共振及其计算和应用[J]. 机械工程学报, 2019, 55(13): 1-12.
[4]	胡超凡, 王衍学. 基于张量分解的滚动轴承复合故障多通道信号降噪方法研究[J]. 机械工程学报, 2019, 55(12): 50-57.
[5]	王方哲, 朱永生, 闫柯, 刘煜炜, 洪军. 滚动轴承内圈温度无线监测技术[J]. 机械工程学报, 2018, 54(22): 8-14.
[6]	陈祥龙, 冯辅周, 张兵志, 江鹏程. 基于平方包络谱相关峭度的最优共振解调诊断滚动轴承故障[J]. 机械工程学报, 2018, 54(21): 90-100.
[7]	杨蕊, 李宏坤, 贺长波, 王奉涛. 利用最优小波尺度循环谱的滚动轴承早期故障特征提取[J]. 机械工程学报, 2018, 54(17): 208-217.
[8]	康守强, 叶立强, 王玉静, 谢金宝,MIKULOVICH V I. 基于数学形态学和IFOA-SVR的滚动轴承可靠度预测方法[J]. 机械工程学报, 2017, 53(8): 201-208.
[9]	鄢小安, 贾民平. 基于改进奇异谱分解的形态学解调方法及其在滚动轴承故障诊断中的应用^*[J]. 机械工程学报, 2017, 53(7): 104-112.
[10]	闫柯, 高闯, 朱永生, 洪军. 基于碲化镉量子点的滚动轴承内圈测温原理与实现技术^*[J]. 机械工程学报, 2017, 53(4): 134-140.
[11]	李宏坤, 杨蕊, 任远杰, 何德鲁, 郭斌. 利用粒子滤波与谱峭度的滚动轴承故障诊断^*[J]. 机械工程学报, 2017, 53(3): 63-72.
[12]	孙鲜明, 刘欢, 赵新光, 周勃. 基于瞬时包络尺度谱熵的滚动轴承早期故障奇异点识别及特征提取^*[J]. 机械工程学报, 2017, 53(3): 73-80.
[13]	卢耀辉, 张醒, 张舒翔, 曾京, 党林媛. 高速列车车体加速寿命试验载荷谱编制及分析[J]. 机械工程学报, 2017, 53(24): 151-160.
[14]	马萍, 张宏立, 范文慧. 基于局部与全局结构保持算法的滚动轴承故障诊断^*[J]. 机械工程学报, 2017, 53(2): 20-25.
[15]	刘志亮, 刘仕林, 李兴林, 康金龙, 彭兴. 滚动轴承安全域建模方法及其在高速列车异常检测中的应用[J]. 机械工程学报, 2017, 53(10): 116-124.

XJTU-SY滚动轴承加速寿命试验数据集解读

XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价