基于LSTM分类器的航空发动机预测性维护模型

doi:10.12305/j.issn.1001-506X.2022.03.39

Abstract

Abstract:

Predictive maintenance using sensor data is a key issue in aeroengine prognostic and health management (PHM). Aiming at the problem of low accuracy of remaining useful life prediction of aeroengine, a predictive maintenance model based on long short-term memory network (LSTM) classifier is proposed. The LSTM classifier fully screens the long time sequence information through the gating unit, and uses the effective information for time sequence prediction. Firstly, a sliding time window is used to prepare training samples. Secondly, the pre-processed samples are input into the LSTM to predict the failure probability of the equipment in a specific time window. Then, by adjusting the window size, a two-class model with the best performance is obtained to better adapt to predictive maintenance requirements. Finally, the National Aeronautics and Space Administration C-MAPSS data set is used to verify the effectiveness of the model. Compared with the existing classification methods, the proposed model is more accurate in rumaining useful life classification.

Key words: prognostic and health management (PHM), predictive maintenance, long short-term memory network (LSTM), time window, binary classification

CLC Number:

TP399

Ruiguan LIN, Huawei WANG, Changchang CHE, Xiaomei NI, Minglan XIONG. Predictive maintenance model of aeroengine based on LSTM classifier[J]. Systems Engineering and Electronics, 2022, 44(3): 1052-1059.

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References 29

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列号	内容
1	发动机单元编号
2	时间循环
3	时间循环1
4	时间循环2
5	时间循环3
6	传感器测量值1
⋮	⋮
26	传感器测量值21

实际	预测
实际	1	0
1	真正类(true positive, TP)	假正类(false positive, FP)
0	假负类(false positive, FN)	真负类(true negative, TN)

Epochs	Batch_size	sequence_length	Validation_split	Dropout	LSTM_1 units	LSTM_2 units
100	200	30	0.05	0.2	100	50

组号	w₀	w₁	时期	准确率	精确率	召回率	F值	AUC	时间/s
1	15	20	30	0.95	0.87	0.81	0.84	0.943	134
2	15	30	25	0.98	0.96	0.96	0.96	0.976	110
3	15	35	30	0.99	0.96	1	0.98	0.968	281
4	15	40	24	0.99	1	0.96	0.98	0.973	103
5	15	45	12	0.97	0.9	1	0.95	0.962	57
6	15	50	27	0.96	0.91	0.97	0.94	0.968	118
7	15	70	19	0.93	0.93	0.91	0.92	0.933	90
8	15	90	28	0.89	0.94	0.85	0.89	0.925	126
9	15	100	25	0.83	0.92	0.82	0.87	0.902	117

编号	方法	准确率	精确率	召回率	F值
1	逻辑回归	0.92	1	0.68	0.81
2	决策树	0.92	0.95	0.72	0.82
3	RF	0.91	0.94	0.68	0.79
4	SVC	0.92	0.95	0.72	0.82
5	KNN	0.92	0.95	0.72	0.82
6	Gaussian NB	0.94	0.83	0.96	0.89
7	本文方法	0.99	1	0.96	0.98

Predictive maintenance model of aeroengine based on LSTM classifier

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