基于CNN-MSLSTM的航空发动机滑油监测方法研究

doi:10.3969/j.issn.1000-1158.2021.02.16

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Abstract Aiming at the defect of traditional data feature extraction method that is difficult to extract the effective features of ECT oil monitoring data,a two-channel network model CNN-MSLSTM based on convolutional neural network (CNN) and multi-scale long-short-term memory (MSLSTM) neural network was proposed. The multi-scale learning was integrated into LSTM. CNN and MSLSTM were used as two channels to learn the characteristics of data in spatial dimension and time dimension. Through the attention mechanism fusion, the wear state of the engine was output by using softmax classifier. The experimental results showed that the classification accuracy of the 3 scale CNN-MSLSTM for ECT data samples is 98%, the F1 score is 98.62%, and the measurement time of single data is only 0.2036ms. The overall performance is better than the single CNN and LSTM networks.

Key words： metrology oil monitoring wear state aeroengine ECT CNN LSTM

Received: 12 March 2019 Published: 18 February 2021

PACS:	TB973
	TG115.28

Fund:The National Natural Science Foundation of China

Corresponding Authors: Wang -Tao E-mail: 1610164135@qq.com

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	MA Min
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Cite this article:

MA Min,WANG Tao. Research on Monitoring Method of AeroengineLubricating Oil Based on CNN-MSLSTM[J]. Acta Metrologica Sinica, 2021, 42(2): 232-238.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.02.16 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I2/232

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