中国机械工程2023，Vol.34Issue(24)：2963-2974,12.DOI:10.3969/j.issn.1004-132X.2023.24.008
融合集成模型与深度学习的机床能耗识别与预测方法
A Method for Identifying and Predicting Energy Consumption of Machine Tools by Combining Integrated Models and Deep Learning
摘要
Abstract
In response to the problems of multi-source influences,high quality feature extraction and selection,complexity and nonlinearity in identification methods for energy consumption proces-ses,a method for identifying and predicting energy consumption of machine tools was proposed by combining integrated models and deep learning.Taking CNC milling as an example,an energy con-sumption model was established based on different cutting periods,and signals were preprocessed by wavelet transform.The preprocessed signals were used to train and predict the energy consumption of the model combining RF and LSTM neural network(RF-LSTM model).Meanwhile,the RF was used to identify the cutting stages and realize the energy consumption classification prediction.The effec-tiveness and superiority of the proposed method were demonstrated through practical cases,and the RF-LSTM model was used to compare with the other four schemes,which verify that this recognition method may accurately predict different operating states and energy consumption of the machine tools.关键词
能耗模型/随机森林/长短时记忆神经网络/能耗预测Key words
energy consumption model/random forest(RF)/long short term memory(LSTM)neural network/energy prediction分类
信息技术与安全科学引用本文复制引用
谢阳,戴逸群,张超勇,刘金锋..融合集成模型与深度学习的机床能耗识别与预测方法[J].中国机械工程,2023,34(24):2963-2974,12.基金项目
国家自然科学基金(52205527,52075229) （52205527,52075229）
江苏省高校自然科学基金(22KJB460018) （22KJB460018）
江苏省"双创博士"人才项目(JSSCBS20221286) （JSSCBS20221286）
