引用本文:王村松,陆宁云,程月华,等.基于无标签、不均衡、初值不确定数据的设备健康评估方法[J].控制与决策,2020,35(11):2687-2695
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基于无标签、不均衡、初值不确定数据的设备健康评估方法
王村松,陆宁云,程月华,姜斌
(南京航空航天大学自动化学院,南京211106)
摘要:
缺少先验知识和完备信息的设备健康评估一直是预测与健康管理(PHM)领域的难点问题.针对设备运行状态观测数据的无标签、不均衡、初值不确定性问题,提出一种多变量深度森林的设备健康评估方法.首先,提出一种基于相关性指标和趋势性指标的特征选择方法以去除冗余特征;然后,利用三维数据标准化和量子模糊聚类方法,动态设定设备健康状态并且解决数据初值的不确定问题;最后,采用一种多变量深度森林分类器实现设备健康状态的离线训练与在线评估.案例分析结果验证了所提出的健康评估方法的有效性和可行性.
关键词:  健康评估  深度森林  量子模糊聚类  特征提取  特征选择
DOI:10.13195/j.kzyjc.2018.1493
分类号:TP206.3
基金项目:国家自然科学基金项目(61873122,61673206);装备预研国防科技重点实验室基金项目(61422080307);航天器在轨故障诊断与维修重点实验室基金项目;江苏省研究生科研与实践创新计划项目(KYCX18_0300).
Equipment health risk assessment based on unlabeled, unbalanced data under uncertain initial condition
WANG Cun-song,LU Ning-yun,CHENG Yue-hua,JIANG Bin
(College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing211106,China)
Abstract:
Equipment health risk assessment without prior knowledge and complete observation information is still a hard problem in the prognostic and health management(PHM) field. To solve the problems caused by unlabeled, unbalanced data under uncertain initial condition upon the health states of engineering equipment, a multivariate deep forest based health assessment method is proposed. Firstly, a correlation and trend metric based feature selection strategy is proposed to remove redundant features. Then, the three-dimensional data is unfolded to be a two-dimensional data, and a quantum fuzzy clustering is utilized to define the health states, which can overcome the problem of initial condition uncertainty. Finally, a multivariate deep forest classifier is adopted for offline training and online health assessment. Case study results show that the proposed health assessment method is effective and feasible.
Key words:  health assessment  deep forest  quantum fuzzy clustering  feature extraction  feature selection

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