一句话：提出用 LSTM 自动生成 CNC 机床预测性诊断模型的系统，可判定刀具与电机轴承状态。
论文针对 CNC 机床预测性维护中诊断模型依赖人工、难以自动生成的问题，提出一套基于机器学习的诊断模型自动生成系统。系统允许机床维护专家选择并运行基于 LSTM 神经网络的模型，以判定 CNC 机床关键部件（如切削刀具和电机轴承）的状态。文中给出了模型在运行过程中的精度变化示例，结果表明切削刀具状态判定精度超过 95%，电机轴承状态判定精度超过 91%。该工作展示了 LSTM 在机床部件状态预测与诊断模型生成中的可用性。
This article presents a description of a system for the automatic generation of predictive diagnostic models of CNC machine tools. This system allows machine tool maintenance specialists to select and operate models based on LSTM neural networks to determine the state of elements of CNC machines. Examples of changes in the accuracy of the models used during operation are given to determine the state of the cutting tool (more than 95%) and the bearings of electric motors (more than 91%).
DOI 原文 ·
@inproceedings{paperbot5914,
title = {A machine learning based approach to autogenerate diagnostic models for CNC machines},
author = {Kamil Adipovich Masalimov},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3418915}
}