PHM专栏

PHM是Prognostic and Health Management 的缩写,即故障预测与健康管理。PHM广泛应用于各个领域。是综合利用现代信息技术、人工智能技术的最新研究成果而提出的一种全新的管理健康状态的解决方案。PHM系统未来一段时间内系统失效可能性以及采取适当维护措施的能力,一般具备故障检测与隔离、故障诊断、故障预测、健康管理和部件寿命追踪等能力。
近年来随着大数据和人工智能算法的兴起,PHM中数据驱动方法以及人工智能在其中的应用越来越受关注。开辟此专栏是为了汇总人工智能算法(主要是传统的机器学习算法和深度学习算法)在PHM中应用的相关学术资源,会不定期更新,持续跟踪相关领域的最新研究成果。也欢迎大家在评论区推荐资源!

一、最新文章

点此进入文献汇总页


二、相关数据集

此部分转载自:https://blog.csdn.net/hustcxl/article/details/89394428

1、CWRU(凯斯西储大学轴承数据中心)

2、MFPT(机械故障预防技术学会)

3、德国Paderborn大学

4、FEMTO-ST轴承数据集

5、辛辛那提IMS

6、XJTU-SY Bearing Datasets(西安交通大学 轴承数据集)

7、东南大学

8、Acoustics and Vibration Database(振动与声学数据库)

参考文献

[1] Smith W A, Randall R B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical Systems and Signal Processing, 2015,64-65:100-131.

[2] Lee D, Siu V, Cruz R, et al. Convolutional neural net and bearing fault analysis[C]//Proceedings of the International Conference on Data Mining (DMIN). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016: 194.

[3] Bin Hasan M. Current based condition monitoring of electromechanical systems. Model-free drive system current monitoring: faults detection and diagnosis through statistical features extraction and support vector machines classification.[D]. University of Bradford, 2013.

[4] Lessmeier C, Kimotho J K, Zimmer D, et al. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification: Proceedings of the European conference of the prognostics and health management society, 2016[C].

[5] Porotsky S, Bluvband Z. Remaining useful life estimation for systems with non-trendability behaviour: Prognostics & Health Management, 2012[C].

[6] Nectoux P, Gouriveau R, Medjaher K, et al. PRONOSTIA: An experimental platform for bearings accelerated degradation tests.: IEEE International Conference on Prognostics and Health Management, PHM’12., 2012[C]. IEEE Catalog Number: CPF12PHM-CDR.

[7] E. S, H. O, A. S S V, et al. Estimation of remaining useful life of ball bearings using data driven methodologies: 2012 IEEE Conference on Prognostics and Health Management, 2012[C].2012

[8] Gousseau W, Antoni J, Girardin F, et al. Analysis of the Rolling Element Bearing data set of the Center for Intelligent Maintenance Systems of the University of Cincinnati: CM2016, 2016[C].

[9] Qiu H, Lee J, Lin J, 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):1066-1090.

[10] B. W, Y. L, N. L, et al. A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings[J]. IEEE Transactions on Reliability, 2018:1-12.

[11] Siyu S , Stephen M A , Ruqiang Y , et al. Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning[J]. IEEE Transactions on Industrial Informatics, 2018:1-1.