仿真数据驱动的长期服役电梯导轨故障迁移诊断方法  

Simulation Data-driven Migration Diagnosis Method for Guide Rail Faults in Long-term Service Elevators

作　　者：肖刚 顾海瑞 董锦锦 王琪冰 陆佳炜 XIAO Gang;GU Hairui;DONG Jinjin;WANG Qibing;LU Jiawei(College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou,310018)

机构地区：[1]中国计量大学机电工程学院,杭州310018

出　　处：《中国机械工程》2024年第1期125-135,共11页China Mechanical Engineering

基　　金：国家自然科学基金(61976193);浙江省“尖兵”研发攻关计划(2023C01022);湖州市重点研发科技计划(2022ZD2019)。

摘　　要：现有的电梯导轨故障诊断研究存在水平振动分类数据稀缺,训练和测试数据集分布差异较大等问题。提出了一种仿真数据驱动的长期服役电梯导轨故障迁移诊断方法。首先构建电梯轿厢的水平动力学模型,将不同类型的导轨故障激励作为系统输入进行仿真,获得丰富的轿厢水平异常振动数据;然后融合残差网络和卷积注意力机制来提取故障特征,采用子领域自适应方法实现无监督场景下源域与目标域条件分布的对齐;最后使用不同工况下的电梯水平振动数据作为目标域对所提方法进行验证。实验结果表明,所提方法在无监督跨域场景下具有较高的故障诊断精度,为解决长期服役电梯的故障数据稀缺问题提供了参考。The existing researches of fault diagnosis of elevator guide rails has some problems,such as scarcity of horizontal vibration classification data and large difference in the distribution of training and test data sets.A simulation data-driven fault migration diagnosis method for long-term service elevator guide rails was proposed.Firstly,the horizontal dynamics model of the elevator car was constructed,different types of guide rail fault excitations as system input for simulation and rich horizontal abnormal vibration data of elevator car were obtained.Secondly,the residual network and convolutional attention mechanism were integrated to extract fault features,and the sub-domain adaptive method was used to align the conditional distribution of source domain and target domain in unsupervised scenarios.Finally,the elevator horizontal vibration data under different working conditions were used as the target domain to verify the proposed method.The experimental results show that the proposed method has high fault diagnosis accuracy in unsupervised cross-domain scenarios,which provides a reference for solving the problems of scarcity of fault data for long-term service elevators.

关 键 词：仿真数据驱动 长期服役电梯 水平振动 子领域自适应 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化] TP391.9[自动化与计算机技术—计算机应用技术]