| Citation: | YANG P D，HUANG H，YU W W，et al. Tool wear prediction based on attention mechanism and PSO-BiLSTM[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（10）：3589-3598 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0545
|
A bi-directional long short-term memory (BiLSTM) neural network optimization end-to-end tool wear prediction method based on attention mechanism (AM) and particle swarm optimization (PSO) algorithm is proposed to address the issues of single monitoring data and poor feature signal processing in tool wear fault diagnosis. Firstly, based on the sensor data, to construct high-quality signal input samples, extract the multi-domain feature. With multi-sensor data to obtain the fused data samples with higher robustness, the Kalman filter is used to fuse the input samples. On this basis, the hyperparameters of the BiLSTM are optimized by PSO, and the neural network model is built according to the optimized hyperparameters. Finally, based on the attention mechanism to give weights to the input influences, the PSO-BiLSTM is improved to obtain a better tool wear prediction. The suggested model's validity and feasibility in tool wear prediction are confirmed by comparative experimental findings, and its accuracy is significantly higher than that of classical deep learning. This approach offers a fresh viewpoint on tool wear prediction.
| [1] |
CHEN Y X, JIN Y, JIRI G. Predicting tool wear with multi-sensor data using deep belief networks[J]. The International Journal of Advanced Manufacturing Technology, 2018, 99(5): 1917-1926.
|
| [2] |
谢振龙, 岳彩旭, 刘献礼, 等. 基于EMD-SVM的钛合金铣削过程刀具磨损监测[J]. 振动、测试与诊断, 2022, 42(5): 988-996.
XIE Z L, YUE C X, LIU X L, et al. Tool wear monitoring based on EMD-SVM in milling process of Ti-alloy[J]. Journal of Vibration, Measurement & Diagnosis, 2022, 42(5): 988-996(in Chinese).
|
| [3] |
ZHOU J J, YU J B. Chisel edge wear measurement of high-speed steel twist drills based on machine vision[J]. Computers in Industry, 2021, 128: 103436.
|
| [4] |
LI L H, AN Q B. An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis[J]. Measurement, 2016, 79(2): 44-52.
|
| [5] |
MORGAN J, O’DONNELL G E. Cyber physical process monitoring systems[J]. Journal of Intelligent Manufacturing, 2018, 29(6): 1317-1328. doi: 10.1007/s10845-015-1180-z
|
| [6] |
戴稳, 张超勇, 孟磊磊, 等. 基于深度学习与特征后处理的支持向量机铣刀磨损预测模型[J]. 计算机集成制造系统, 2020, 26(9): 2331-2343.
DAI W, ZHANG C Y, MENG L L, et al. Support vector machine milling wear prediction model based on deep learning and feature re-processing[J]. Computer Integrated Manufacturing Systems, 2020, 26(9): 2331-2343(in Chinese).
|
| [7] |
郑圆, 胡建中, 贾民平, 等. 一种基于参数优化变分模态分解的滚动轴承故障特征提取方法[J]. 振动与冲击, 2020, 39(21): 195-202.
ZHENG Y, HU J Z, JIA M P, et al. A method for rolling bearing fault feature extraction based on parametric optimization VMD[J]. Journal of Vibration and Shock, 2020, 39(21): 195-202(in Chinese).
|
| [8] |
李聪波, 万腾, 陈行政, 等. 基于切削功率的数控车削批量加工刀具磨损在线监测[J]. 计算机集成制造系统, 2018, 24(8): 1910-1919.
LI C B, WAN T, CHEN X Z, et al. On-line monitoring method of tool wear for NC turning in batch processing based on cutting power[J]. Computer Integrated Manufacturing Systems, 2018, 24(8): 1910-1919(in Chinese).
|
| [9] |
何彦, 凌俊杰, 王禹林, 等. 基于长短时记忆卷积神经网络的刀具磨损在线监测模型[J]. 中国机械工程, 2020, 31(16): 1959-1967.
HE Y, LING J J, WANG Y L, et al. In-process tool wear monitoring model based on LSTM-CNN[J]. China Mechanical Engineering, 2020, 31(16): 1959-1967(in Chinese).
|
| [10] |
曹大理, 孙惠斌, 张纪铎, 等. 基于卷积神经网络的刀具磨损在线监测[J]. 计算机集成制造系统, 2020, 26(1): 74-80.
CAO D L, SUN H B, ZHANG J D, et al. In-process tool condition monitoring based on convolution neural network[J]. Computer Integrated Manufacturing Systems, 2020, 26(1): 74-80(in Chinese).
|
| [11] |
王丽华, 杨家巍, 张永宏, 等. 基于堆叠降噪自编码的刀具磨损状态识别[J]. 中国机械工程, 2018, 29(17): 2038-2045. doi: 10.3969/j.issn.1004-132X.2018.17.004
WANG L H, YANG J W, ZHANG Y H, et al. Tool wear condition recognition based on SDAE[J]. China Mechanical Engineering, 2018, 29(17): 2038-2045(in Chinese). doi: 10.3969/j.issn.1004-132X.2018.17.004
|
| [12] |
LUO J H, ZHANG X. Convolutional neural network based on attention mechanism and Bi-LSTM for bearing remaining life prediction[J]. Applied Intelligence, 2022, 52(1): 1076-1091. doi: 10.1007/s10489-021-02503-2
|
| [13] |
ALJAMAL M A, ABDELGHAFFAR H M, RAKHA H A. Developing a neural-Kalman filtering approach for estimating traffic stream density using probe vehicle data[J]. Sensors, 2019, 19(19): 4325. doi: 10.3390/s19194325
|
| [14] |
沈涛, 李舜酩. 针对滚动轴承故障的批标准化CNN-LSTM诊断方法[J]. 计算机集成制造系统, 2022, 28(12): 3946-3955.
SHEN T, LI S M. CNN-LSTM method with batch normalization for rolling bearing fault diagnosis[J]. Computer Integrated Manufacturing Systems, 2022, 28(12): 3946-3955(in Chinese).
|
| [15] |
吴飞, 农皓业, 马晨浩. 基于PSO-LSTM模型的刀具磨损预测方法[J]. 吉林大学学报(工学版), 2023, 53(4): 984-997.
WU F, NONG H Y, MA C H. Tool wear predictionmethod based on PSO-LSTM model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 984-997(in Chinese).
|
| [16] |
ZHANG J S, JIANG Y C, WU S M, et al. Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism[J]. Reliability Engineering & System Safety, 2022, 221: 108297.
|
| [17] |
PHM Society. PHM data challenge 2010[DB/OL]. (2023-07-25)[2010-05-18]. http://www.phmsociety. org/competition/phm/10.
|
Figures(11) / Tables(4)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
