基于RCNN-ABiLSTM的机械设备剩余寿命预测方法

doi:10.12305/j.issn.1001-506X.2023.03.35

Abstract

Abstract:

Aiming at the problem that the key degradation information of mechanical equipment is easy to be submerged in nonlinear, multi-dimensional, long-term and large-scale monitoring data, a method for predicting the remaining useful life of mechanical equipment based on residual convolutional neural network-attentional bidirectional long short-term memory network(RCNN-ABiLSTM) is proposed. Firstly, the RCNN is trained for deep spatial feature extraction of the monitoring data.Then, by introducing the attention mechanism, the weight parameters of the time-related features extracted by BiLSTM are optimized. And the expression of the key degradation information on the remaining life prediction is strengthened. Finally, the effectiveness of the proposed method is verified by the aircraft engine. The analysis results show that the proposed method can accurately find the degradation time point for multi-dimensional monitoring data with complex operating conditions and variable failure modes. The remaining useful life prediction accuracy of long-running equipment is effectively improved.

Key words: residual convolutional neural network (RCNN), attention mechanism, fusion model, remaining useful life (RUL) prediction, aircraft engine

CLC Number:

TP183

Xiaojia YAN, Weige LIANG, Gang ZHANG, Bo SHE, Fuqing TIAN. Prediction method for mechanical equipment based on RCNN-ABiLSTM[J]. Systems Engineering and Electronics, 2023, 45(3): 931-940.

Figures/Tables 15

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发动机	类型	发动机台数	数据总量	工作状态	故障模式
FD001	训练集	100	20 631	1	1
FD001	测试集	100	13 096	1	1
FD002	训练集	260	53 759	6	1
FD002	测试集	259	33 991	6	1
FD003	训练集	100	24 720	1	2
FD003	测试集	100	16 596	1	2
FD004	训练集	249	61 249	6	2
FD004	测试集	248	41 214	6	2

参数	取值	参数	取值
FD001到FD004的S	30/20/30/15	边缘填充	same
RCNN卷积核大小	2×1	优化器	Adam
RCNN的激活函数	tanh	学习率	0.002
RCNN层数	5	批尺寸	128
LSTM神经元数目	64	迭代次数	80
LSTM层数	1	Dropout率	0.25

方法	FD001		FD002		FD003		FD004
方法	RMSE	Score	RMSE	Score	RMSE	Score	RMSE	Score
SVR^[15]	20.96	1 382	35.96	18 900	21.64	2 956	36.54	10 023
CNN^[15]	18.45	1 290	30.29	13 600	19.82	1 600	29.16	7 890
LSTM^[17]	16.14	338	24.49	4 450	16.18	852	28.17	5 550
CNN-LSTM^[30]	16.13	303	20.46	3 440	17.12	1 420	23.26	4 630
Autoencoder-BiLSTM^[20]	13.63	261	19.32	3 560	14.21	285	22.45	4 886
RCNN-ABiLSTM	12.98	258	19.16	2 980	13.24	246	22.29	3 795

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Prediction method for mechanical equipment based on RCNN-ABiLSTM

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