机械工程学报 ›› 2019, Vol. 55 ›› Issue (8): 1-13.doi: 10.3901/JME.2019.08.001
• 仪器科学与技术 • 下一篇
裴洪, 胡昌华, 司小胜, 张建勋, 庞哲楠, 张鹏
收稿日期:
2018-09-12
修回日期:
2019-02-25
出版日期:
2019-04-20
发布日期:
2019-04-20
通讯作者:
胡昌华(通信作者),男,1966年出生,博士,教授,博士研究生导师,长江学者特聘教授,国家教学名师。主要研究方向为故障诊断、容错控制、寿命预测与健康管理。E-mail:hch_reu@sina.com
作者简介:
裴洪,男,1992年出生,博士研究生。主要研究方向为装备的剩余寿命预测与维修决策。E-mail:ph2010hph@sina.com
基金资助:
PEI Hong, HU Changhua, SI Xiaosheng, ZHANG Jianxun, PANG Zhenan, ZHANG Peng
Received:
2018-09-12
Revised:
2019-02-25
Online:
2019-04-20
Published:
2019-04-20
摘要: 随着科学技术的发展和生产工艺的进步,当代设备日益朝着大型化、复杂化、自动化以及智能化方向发展。为保障设备安全性与可靠性,剩余寿命(Remaining useful life,RUL)预测技术受到了普遍关注,同时得到了广泛应用。传统的统计数据驱动方法受模型的选择影响明显,而机器学习具有强大的数据处理能力,并且无需确切的物理模型和专家先验知识,因而机器学习在剩余寿命预测领域表现出了广阔的应用前景。鉴于此,详细分析和阐述了基于机器学习的设备剩余寿命预测方法。根据机器学习模型结构的深度,将其分为基于浅层机器学习的方法和基于深度学习的方法。同时疏理了每类方法的发展分支与研究现状,并且总结了相应的优势和缺点,最后探讨了基于机器学习的剩余寿命预测方法的未来研究方向。
中图分类号:
裴洪, 胡昌华, 司小胜, 张建勋, 庞哲楠, 张鹏. 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): 1-13.
PEI Hong, HU Changhua, SI Xiaosheng, ZHANG Jianxun, PANG Zhenan, ZHANG Peng. Review of Machine Learning Based Remaining Useful Life Prediction Methods for Equipment[J]. Journal of Mechanical Engineering, 2019, 55(8): 1-13.
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