基于多尺度自适应注意力网络的剩余寿命预测

doi:10.7527/S1000-6893.2022.26918

Abstract

Abstract:

Remaining useful life prediction is the key to and premise of intelligent maintenance decision-making of complex equipment systems. The potential relationship among different index data brings challenges to prediction accuracy， while parameter selection also increases model prediction difficulty. We use the multi-scale adaptive attention network method to fuse the feature relationship among data from vertical and horizontal dimensions， respectively， and give the piecewise nonlinear target function to improve the prediction accuracy and reduce the root mean square error. The adaptive mechanism is employed to automatically select the size of convolution kernel， improving the efficiency of network training. Empirical analysis of Company-Modular aero-propulsion system simulation data sets prove the effectiveness of this method in remaining useful life prediction of complex systems.

Key words: remaining useful life, multi-scale learning, adaptive attention, nonlinear target function, complex systems

CLC Number:

V235.13

Bin LIU, Jing XU, Meiling HUO, Xueying CUI, Xiufeng XIE, Donghui YANG, Jia WANG. Remaining useful life prediction based on multi-scale adaptive attention network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(5): 226918.

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References 24

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数据集	数量
数据集	FD001	FD002	FD003	FD004
训练集的发动机	100	260	100	249
测试集的发动机	100	259	100	248
总训练样本	20 631	53 579	24 720	61 249
总测试样本	13 096	33 991	16 596	41 214
失效模式	1	1	2	2
运行条件	1	6	1	6

数据集	K个传感器数据	抛弃指标的索引
FD001	14	2， 3， 4， 5， 9， 10， 14， 20， 22， 23
FD002	15	2， 3， 4， 5， 9， 10， 20， 22， 23
FD003	21	2， 3， 4
FD004	21	2， 3， 4

数据集	输入样本大小	训练样本数	验证样本数
FD001	40×14	11 711	5 020
FD002	40×15	30 533	13 086
FD003	50×21	15 273	6 547
FD004	50×21	34 333	14 715

参数	数值
3条支路上卷积层的核大小	10， 15， 20
扩张卷积的卷积核大小	2
扩张卷积的扩张率	2
两个全连接层卷积滤波器的个数	80， 60
时间窗口大小	40， 40， 50， 50
3条支路的卷积核个数	10， 10， 10
Dropout率	0.2

方法	RMSE
方法	FD001	FD002	FD003	FD004
CNN^［5］	18.45	30.29	19.82	29.16
DLSTM^［11］	16.14	24.49	16.18	28.17
DCNN^［6］	12.61	22.36	12.64	23.31
DSCN^［24］	10.95	20.47	10.62	22.64
MSCNN^［9］	11.44	19.35	11.67	22.22
MLE+CCF^［19］	11.57	18.84	11.83	20.78
MAAN	10.80	14.79	10.93	16.78

Remaining useful life prediction based on multi-scale adaptive attention network

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数据集	RMSE
数据集	线性	非线性
FD001	13.20±0.30	10.80±0.15
FD002	17.38±0.83	14.79±0.36
FD003	13.63±0.54	10.93±0.44
FD004	19.63±0.60	16.78±0.80

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[3]	Chao REN, Huiqin LI, Tianmei LI, Jianxun ZHANG, Xiaosheng SI. Equipment remaining useful life prediction method with dynamic calibration of degradation model [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(19): 228345-228345.
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[13]	SHANG Yongshuang, LI Wenhai, LIU Changjie, SHENG Pei. Prediction of Remaining Useful Life for Equipment with Partially Observed Information [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, (5): 848-854.
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方法	评分函数值
方法	FD001	FD002	FD003	FD004
CNN^［5］	1 286.7	13 570	1 596.2	7 886.4
DLSTM^［11］	338	4 450	852	5 550
DCNN^［6］	273.7	10 412	284.1	12 466
DSCN^［24］	260.67	4 367.56	246.55	5 168.64
MSCNN^［9］	196.22	3 747	241.89	4 844
MLE+CCF^［19］	208	1 606	262	2 081
MAAN	183.77	901.71	214.17	1 189.11

数据集	评分函数值
数据集	线性	非线性
FD001	303.13±18.98	183.77±7.54
FD002	1 708.45±474.15	901.71±122.36
FD003	380.06±87.52	214.17±26.65
FD004	1 854.25±415.17	1 189.11±107.43