基于相似样本特征提取的装备性能退化研究

doi:10.12305/j.issn.1001-506X.2022.07.35

Abstract

Abstract:

In order to monitor the performance change rule of equipment in real time and predict the possible emergencies, this paper firstly considers the problems of extensive data sources and non-standard data forms in the online monitoring and warning system of engineering equipment, and then organizes the historical data based on the B-spline interpolation algorithm. Secondly, in view of the random error and irregular change of performance indicators in performance degradation, this paper extracts and reconstructs degradation characteristics of similar sample sets based on self-organizing mapping and stacked autoencoder. An index construction method based on minimum characteristic circle is proposed. The double exponential model is adopted to predict the performance degradation law and life of the equipment. Finally, the simulation data are used to validate the correctness of the model. The results show that the model and method can effectively predict the performance degradation law of equipment, and can reflect the performance degradation and recovery law of equipment under impact.

Key words: data specification, self-organizing map, similar sample feature, the least characteristic circle, double exponential model

CLC Number:

TB114.3

Dongdong ZHANG, Xiaochuan AI, Chang LIU. Research on equipment performance degradation based on feature extraction of similar samples[J]. Systems Engineering and Electronics, 2022, 44(7): 2374-2380.

Figures/Tables 7

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测试样本	实验寿命/h	本文模型		深度学习
测试样本	实验寿命/h	估计寿命/h	误差/%	估计寿命/h	误差/%
c-1 c-2 c-3	2 262 1 854 2 142	2 231 1 913 2 106	1.3 2.9 1.6	2 184 1 803 1 983	5.3 4.6 7.4

Research on equipment performance degradation based on feature extraction of similar samples

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