农业装备与车辆工程2026，Vol.64Issue(3)：91-96,115,7.DOI:10.3969/j.issn.1673-3142.2026.03.015
基于CNN-LSTM的机械手臂关节内部轴承故障诊断方法
Diagnosis method of joint internal bearing fault in robotic arm based on CNN-LSTM
摘要
Abstract
To address the issues of strong subjectivity,significant lag,and low accuracy in traditional fault diagnosis methods for robotic joint bearings,a fault diagnosis method based on the fusion of a Convolutional Neural Network(CNN)and a Long Short-Term Memory(LSTM)network was proposed.Vibration signals were collected using piezoelectric acceleration sensors.After undergoing window segmentation,wavelet denoising,and standardized preprocessing,the signals were fed into a dual-branch parallel network.Local impact features and temporal dynamic features were extracted separately and then fused through a connection layer.Hyperparameters were optimized using a chaos algorithm,ultimately enabling fault classification.Experimental results showed that the system achieved an accuracy of 98.70％with a processing time of 0.012 s per sample.Its performance was superior compared to single models and unoptimized fusion models,and it demonstrated better real-time performance than the 2D-CNN method.This approach effectively enables high-precision,real-time fault diagnosis for robotic joint bearings,offering valuable reference for ensuring the continuity of industrial production.关键词
深度学习/机械手臂/轴承/故障诊断/卷积神经网络/长短期记忆网络Key words
deep learning/mechanical arm/bearing/fault diagnosis/convolutional neural network/long short-term memory network分类
机械制造引用本文复制引用
王佳,窦耀..基于CNN-LSTM的机械手臂关节内部轴承故障诊断方法[J].农业装备与车辆工程,2026,64(3):91-96,115,7.基金项目
2025年教育部产学合作协同育人项目"新工科背景下智能制造工程专业实践教学改革研究"(250302436281719) （250302436281719）
江苏省工程师学会2025年度研究课题"产教融合背景下现场工程师培养体系创新与实践研究"(JSIE2025KT05) （JSIE2025KT05）
高校毕业生就业协会2025年度高等学校教育数字化转型研究专项课题"基于数字孪生技术的机器人工程专业个性化教育教学研究与实践"(GJX25Z2101) （GJX25Z2101）
中国机械工业教育协会2025年度课题"人工智能技术赋能'双师型'教师数字素养培养路径研究"(ZJJX25SY016) （ZJJX25SY016）
2024年江苏省大学生创新创业训练计划项目"基于PLC技术的协作机器人机械手臂自动化控制系统设计"(202413988009Y) （202413988009Y）
