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| 1 | 转向架关键运动部件动力学机理与故障诊断研究综述显示文摘转向架是机车车辆的关键子系统之一,对保证列车的运行性能和服役安全具有重要的作用。随着列车运行速度的提高,轮轨之间的动态相互作用加剧,导致轮对、轴箱轴承、齿轮传动系统、牵引电机等转向架关键运动部件的运行工况十分恶劣,故障时有发生,危及行车安全。针对转向架关键运动部件的动力学行为和故障诊断,国内外学者开展了大量的研究工作。首先,从动力学模型、故障机理分析两个方面总结了转向架关键运动部件的研究现状。其次,梳理了转向架关键运动部件故障诊断中常用的信号处理和机器学习方法。最后,面向智能运维的工程需求,对当前研究存在的问题和发展趋势进行了总结和展望。 | 杨绍普 顾晓辉 刘永强 邓飞跃 刘泽潮 刘文朋 王宝森 | 2023 | 机械工程学报2023，59，20： | 0 |
| 2 | Machinery fault diagnostic method based on numerical simulation driving partial transfer learning显示文摘Artificial intelligence(AI),which has recently gained popularity,is being extensively employed in modern fault diagnostic research to preserve the reliability and productivity of machines.The effectiveness of AI is influenced by the quality of the labeled training data.However,in engineering scenarios,available data on mechanical equipment are scarce,and collecting massive amounts of well-annotated fault data to train AI models is expensive and difficult.In response to the inadequacy of training samples,a numerical simulation-based partial transfer learning method for machinery fault diagnosis is proposed.First,a suitable simulation model of critical components in a mechanical system is developed using the finite element method(FEM),and numerical simulation is performed to acquire FEM simulation samples containing different fault types.Second,several synthetic simulation samples are generated to form complete source domain training samples using a generative adversarial network.Subsequently,the partial transfer learning network is trained to extract shared fault characteristics between the simulation and measured samples in the case of class imbalance.Finally,the resulting model is used to diagnose unknown samples from real-world mechanical systems in operation.The proposed method is tested on actual fault samples of bearings and gears obtained from a public dataset and experimental test rig available in our laboratory,achieving average classification accuracy of 99.54%and 99.64%,respectively.Comparison investigations reveal that the proposed method has superior classification and generalization ability when detecting faults in real mechanical systems. | LOU YunXia KUMAR Anil XIANG JiaWei | 2023 | Science China(Technological Sciences)2023，66，12： | 0 |
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