基于多传感器融合的刀具剩余寿命预测

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (17) : 47-54.

论文

基于多传感器融合的刀具剩余寿命预测

刘思辰1,2,杨飞然1,杨军1,2

作者信息 +

Tool residual life prediction based on multi-sensor fusion

LIU Sichen1,2, YANG Feiran1, YANG Jun1,2

Author information +

文章历史 +

摘要

为了提高刀具磨损状态的预测精度，提出了一种基于多传感器融合的刀具剩余寿命预测方法。在训练阶段，首先联合振动、电流、以及PLC控制器信息进行数据预处理并利用时间序列分析等方法进行特征提取。然后针对单帧样本缺乏上下文信息且无法全部覆盖整个生命周期数据的问题，采用多帧联合与mix-up方法对数据进行增强。最后设计一个深度神经网络来学习多模态输入特征与刀具剩余寿命之间的复杂非线性函数。在测试阶段，对网络输出结果进行中值滤波去除噪声影响得到最终预测值。试验结果表明，多模态数据的使用与数据增强的引入均可显著提升刀具磨损量的预测精度。

Abstract

Here, to improve the prediction accuracy of tool wear state, a tool residual life prediction method based on multi-sensor fusion was proposed.In training phase, firstly, combining vibration, current and PLC controller information, data were preprocessed, and the time series analysis method was used to do feature extraction; then, aiming at the problem of single-frame sample lacking context information and being not able to cover the whole life cycle data, multi-frame combination and the mix-up method were used to enhance data; finally, a deep neural network was designed to learn complex nonlinear functions among multi-modal input features and tool residual life.In test phase, median filtering was used to remove effects of noise and obtain the final predicted value.The experimental results showed that the effectiveness of multi-sensor fusion is verified; using multi-modal data and introducing data enhancement can significantly improve the prediction accuracy of tool wear.

导出引用

刘思辰,杨飞然,杨军. 基于多传感器融合的刀具剩余寿命预测[J]. 振动与冲击, 2021, 40(17): 47-54

LIU Sichen, YANG Feiran, YANG Jun. Tool residual life prediction based on multi-sensor fusion[J]. Journal of Vibration and Shock, 2021, 40(17): 47-54

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