基于神经网络的设备有效使用时间估计方法

Bulletin of the Saint Petersburg State Institute of Technology (Technical University) Pub Date : 2021-01-01 DOI:10.36807/1998-9849-2021-59-85-107-112

M. Dli, A. Puchkov, E. Lobaneva

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引用次数: 0

摘要

提出了一种基于并行循环神经网络和卷积神经网络对诊断数据进行处理的设备使用时间预测方法。卷积网络的图像是在诊断数据的小波变换基础上形成的。神经网络工作在多值分类模式下，该方法采用递推最小二乘法对设备使用时间的预测进行细化。文中给出了在MatLAB环境下开发的实现该方法的程序进行模型实验的结果。

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METHOD FOR ESTIMATING THE TIME OF USEFUL USE OF EQUIPMENT BASED ON NEURAL NETWORKS

A method for predicting the useful time of equipment based on the processing of diagnostic data using parallel recurrent and convolutional neural networks is proposed. Images for the convolutional network are formed on the basis of the wavelet transform of diagnostic data. Neural networks operate in a multivalued classification mode, which is used in the method to refine the prediction of the useful time of equipment based on the recursive least squares method. The results of a model experiment performed using a program developed in the MatLAB environment that implements the proposed method are presented.

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Bulletin of the Saint Petersburg State Institute of Technology (Technical University)

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