机电工程技术2024，Vol.53Issue(7)：18-22,5.DOI:10.3969/j.issn.1009-9492.2024.07.004
基于SAConvFormer算法的焊接故障诊断在非平衡数据集上的应用
Application of SAConvFormer Algorithm in Welding Fault Diagnosis on Imbalanced Datasets
摘要
Abstract
Predictive maintenance plays a crucial role in the manufacturing industry,with effective maintenance of welding equipment being particularly pivotal for reducing corporate expenditures and achieving the goal of unmanned workshops.However,research in the welding domain remains in its early stages,and the application of deep learning in this field is relatively limited.Addressing this issue,a fault diagnosis model is proposed based on the Spatial Attention Convolutional Transformer(SAConvFormer)to address the challenge of predicting welding equipment faults.By collecting data from the welding process,this algorithm enhances convolutional neural networks through a spatial attention mechanism,thereby more accurately predicting various fault types in welding processes.Experimental results demonstrate that the SAConvFormer model achieves a recall rate of 95% with an error margin of only 2% for predicting normal fault types.For welding deviation fault types,the model maintains a stable recall rate of approximately 80%,while the prediction accuracy for incomplete fusion fault types is relatively lower but still exceeds 70% .Compared to traditional algorithms,the SAConvFormer model exhibits excellent performance in terms of recall rate.This research not only represents technological advancement but also provides a novel and effective approach for fault diagnosis in welding equipment,with significant theoretical and practical implications.关键词
预测性维护/焊接设备/故障诊断/空间注意力机制/深度学习Key words
predictive maintenance/welding equipment/fault diagnosis/spatial attention mechanism/deep learning分类
矿业与冶金引用本文复制引用
付惠斌,李晨,陈翀..基于SAConvFormer算法的焊接故障诊断在非平衡数据集上的应用[J].机电工程技术,2024,53(7):18-22,5.基金项目
国家自然基金资助项目(62302103) （62302103）
