机电工程技术2026，Vol.55Issue(16)：63-69,7.DOI:10.3969/j.issn.1009-9492.2025.00139
基于RepLKNet-BiGRU的起重机齿轮箱故障诊断方法
Crane Gearbox Fault Diagnosis Method Based on RepLKNet-BiGRU
摘要
Abstract
To address the issue that crane gearboxes are prone to failures under complex operating conditions and that traditional diagnostic methods have limited feature extraction capability,a multi-modal fault diagnosis model integrating two-dimensional images and one-dimensional vibration signals is proposed.The model first converts the raw vibration signals into 2D image representations with global temporal dependencies using the Gramian Angular field method,thus preserving the evolutionary features of the time series in the visual space.Subsequently,the model employs RepLKNet and BiGRU-Global Attention structures for deep extraction of features from different modalities,respectively.Among them,RepLKNet is able to fully mine the spatial pattern and local structure information of the image by virtue of its large receptive field convolution structure.BiGRU network captures the before-and-after temporal correlation of the signal through the bi-directionally gated recurrent unit and highlights the important dynamic changes of the key time segments by combining with the global attention mechanism.After the two modal features are fused at the feature layer,the accurate identification of different fault types of gearboxes is achieved through the fully connected layer with Softmax classifier.The experimental results show that the proposed model exhibits excellent classification accuracy and robustness with a 98.47%accuracy under multiple typical gearbox fault conditions,which is significantly better than the single-modal or traditional deep learning models.关键词
起重机齿轮箱/故障诊断/多模态/RepLKNet/Global AttentionKey words
crane gearbox/fault diagnosis/multi-modal/RepLKNet/Global Attention分类
机械制造引用本文复制引用
李云龙,汤秀丽,潘俊萍,刘武胜,黄越峰,刘静,李志农..基于RepLKNet-BiGRU的起重机齿轮箱故障诊断方法[J].机电工程技术,2026,55(16):63-69,7.基金项目
中国机械工业集团有限公司青年基金(QNJJ-PY-2024-30) （QNJJ-PY-2024-30）
北京市博士后工作经费资助项目(2024-67) （2024-67）
北京起重运输机械设计研究院有限公司重点研发项目(25KJ24215) （25KJ24215）
