摘要
针对传统相似性方法忽略设备故障模式、退化速度以及监测数据长度间差异性的问题,提出多故障模式下多尺度相似性集成(MFM-MSEN)方法,以提高寿命预测精度并表征预测不确定性.通过训练故障分类模型,设计时序加权预测策略,识别设备故障模式,实现训练与测试设备间的分类匹配并降低匹配复杂度.在此基础上提出多尺度集成策略,可克服单尺度方法的数据利用率限制,并增强预测泛化性能,在多个尺度上匹配健康指标间的相似性,进一步采用核密度估计集成多尺度预测结果,以高精度拟合剩余寿命概率分布.实验结果证明,MFM-MSEN方法具有应对设备退化差异的优越性.
Traditional similarity-based methods generally ignore the diversity of equipment fault modes, the difference in degradation rates, and the inconsistency among monitoring data lengths. Thus, a similarity-based multi-scale ensemble method in multiple fault modes(MFM-MSEN) is proposed to improve remaining useful life(RUL) prediction accuracy and characterize prediction uncertainty. By training the fault mode classification model, designing the time-series weighted prediction strategy, and recognizing the fault mode of equipment, the test equipment is matched with the training equipment with the same fault mode to reduce matching complexity, based on which, a multi-scale ensemble strategy is proposed to overcome the data utilization limitation caused by single-scale matching methods and enhance the generalization ability of the proposed MFM-MSEN method. This strategy matches the similarities between test equipment and training equipment at multiple time scales, and then multiscale prediction results are integrated to fit accurate RUL probability distribution by employing kernel density estimation. Experimental results demonstrate the superiority of the proposed MFM-MSEN method in dealing with the differences in equipment degradation.
作者
舒俊清
许昱晖
夏唐斌
潘尔顺
奚立峰
SHU Junqing;XU Yuhui;XIA Tangbin;PAN Ershun;XI Lifeng(State Key Laboratory of Mechanical System and Vibration,School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2022年第5期564-575,共12页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金(51875359)
上海市自然科学基金(20ZR1428600)
教育部-中国移动科研基金研发项目(CMHQ-JS-201900003)
临港地区智能制造专项(ZN2017020102)
上海商用飞机系统工程科创中心联合研究基金(FASE-2021-M7)。
关键词
剩余使用寿命
故障模式识别
多尺度集成策略
核密度估计
相似性方法
remaining useful life(RUL)
fault mode identification
multiscale ensemble strategy
kernel density estimation
similarity-based method
