噪声与振动控制2025，Vol.45Issue(5)：131-137,7.DOI:10.3969/j.issn.1006-1355.2025.05.021
塔式起重机小样本条件下结构损伤智能诊断方法研究
Intelligent Diagnosis Method of Structural Damages under Small Sample Conditions of Tower Cranes
摘要
Abstract
Intelligent diagnosis of tower crane structure damages is an important guarantee for its efficient and stable operation.However,due to the strict maintenance system of the tower cranes,the actual damage samples are relatively few,leading to that the model based on data-driven can only learn very limited diagnosis knowledge,resulting in a significant de-crease of the diagnosis accuracy.In response to this problem,based on the feature extraction concept of comparative learning was introduced,an intelligent diagnostic method of structure damage was proposed under the condition of small samples of tower cranes.This method is mainly performed by constructing a kernel guided contrastive learning loss function and a typi-cal classification loss function.Among them,the kernel guided contrastive learning loss function can map features to an infi-nite dimensional space through a Gaussian kernel for sample pair metric learning,and use hyper-parameters to control the bandwidth of the classification boundary.Finally,the effectiveness of the proposed method was verified through the physical simulation model experimental platform of the tower crane and damage samples collected from real service tower cranes.The experimental results show that the proposed method has robustness and superiority in diagnosis of tower crane damage under small sample conditions compared to deep learning models and standard contrastive learning models.关键词
故障诊断/塔式起重机/对比学习/小样本Key words
fault diagnosis/tower cranes/contrastive learning/small sample分类
机械制造引用本文复制引用
杨蕊,安增辉,宋世军,孟祥林,张茹,黄文武..塔式起重机小样本条件下结构损伤智能诊断方法研究[J].噪声与振动控制,2025,45(5):131-137,7.基金项目
国家自然科学基金资助项目(52005300),山东省高等学校青创科技支持计划资助项目(2023KJ124) （52005300）
山东省自然科学基金资助项目(ZR2024QE083) （ZR2024QE083）
