面向多故障模式的多尺度相似性集成寿命预测

doi:10.16183/j.cnki.jsjtu.2021.024

上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (5): 564-575.doi: 10.16183/j.cnki.jsjtu.2021.024

面向多故障模式的多尺度相似性集成寿命预测

舒俊清, 许昱晖, 夏唐斌(), 潘尔顺, 奚立峰

上海交通大学机械系统与振动国家重点实验室;机械与动力工程学院,上海 200240

收稿日期:2021-01-25 出版日期:2022-05-28 发布日期:2022-06-07
通讯作者: 夏唐斌 E-mail:xtbxtb@sjtu.edu.cn
作者简介:舒俊清(1999-),女,安徽省安庆市人,硕士生,主要研究方向为智能预测与健康管理.
基金资助:
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)

A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes

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

Received:2021-01-25 Online:2022-05-28 Published:2022-06-07
Contact: XIA Tangbin E-mail:xtbxtb@sjtu.edu.cn

摘要/Abstract

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.

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

Abstract:

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.

Key words: remaining useful life (RUL), fault mode identification, multiscale ensemble strategy, kernel density estimation, similarity-based method

中图分类号:

TH17

舒俊清, 许昱晖, 夏唐斌, 潘尔顺, 奚立峰. 面向多故障模式的多尺度相似性集成寿命预测[J]. 上海交通大学学报, 2022, 56(5): 564-575.

SHU Junqing, XU Yuhui, XIA Tangbin, PAN Ershun, XI Lifeng. A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes[J]. Journal of Shanghai Jiao Tong University, 2022, 56(5): 564-575.

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数据集	子集	发动机数量	发动机最大运行周期	发动机最小运行周期	运行工况种类	故障模式
FD003	训练集	100	525	145	1	2
	测试集	100	475	38
FD004	训练集	249	543	128	6	2
	测试集	248	486	19

编号	结构名称	网络层	神经元数量	激励函数
1	编码器	输入层	14	ReLU
2		隐含层 1	7	ReLU
3		隐含层 2	1	Linear
4	解码器	隐含层 3	7	ReLU
5		输出层	14	tanh

数据集	时间尺度集合 {τ₁,τ₂,τ₃,τ₄}	组别 1({τ₁}) 发动机数量	组别 2({τ₁,τ₂}) 发动机数量	组别 3({τ₁,τ₂,τ₃}) 发动机数量	组别 4({τ₁,τ₂,τ₃,τ₄}) 发动机数量
FD003	{38, 58, 78, 98}	6	6	9	79
FD004	{19, 39, 59, 79}	15	10	17	206

方法种类	方法名称	RMSE			Score
方法种类	方法名称	FD003	FD004	总计	FD003	FD004	总计
基于相似性的方法	MFM-MSEN 方法	15.07	22.22	20.42	609.97	2641.05	3251.02
	传统单尺度方法	16.89	24.62	22.67	1280.43	5009.31	6289.74
	EN^[3]	19.16	22.15	21.33	1727	2901	4628
	BiLSTM-ED^[15]	17.48	23.49	21.93	574	3202	3776
	RULCLIPPER^[20]	16.00	24.33	22.26	317	3132	3449
RUL直接映射法	DCNN^[6]	12.64	23.31	20.81	284.1	12466	12750.1
	D-LSTM^[7]	16.18	28.17	25.31	852	5550	6402
	MODBNE^[8]	12.51	28.66	25.11	421.91	6557.62	6979.53

指标	FD003			FD004
指标	RMSE	Score	匹配时间/s	RMSE	Score	匹配时间/s
E_MSEN	15.77	1247.49	106	23.68	3808.86	718
E_MFM-MSEN	15.07	609.97	49	22.22	2641.05	374
Λ/%	4.43	51.1	53.8	6.17	30.7	47.9

面向多故障模式的多尺度相似性集成寿命预测

A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes

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