ABB传动在汽车变速箱测试台中的应用

汽车工业的发展离不开测试设备,本文重点介绍了变速箱测试台的传动配置,提出了ABB变频器DTC控制技术在应用中的主要特点,详细阐明了传动的几种工况,并讨论了可选的传动方案。     

轻载装载机变速箱振动特性分析与试验

为了给装载机变速箱减振降噪提供依据,通过集中参数和有限元法相结合的方式对变速箱进行振动分析。综合考虑液力变矩器激励、齿轮系统内部激励等因素,建立装载机变速箱传动系统的弯-扭耦合动力学模型,求解得到各轴承的动态支反力;建立箱体有限元模型,进行模态分析,以轴承动态支反力为激励,在模态的基础上进行箱体谐响应分析,得到箱体在激励下的振动响应,选择箱体表面的振动测点,分析测点的振动加速度,找到并分析振动峰值及对应振型;最后进行变速箱振动试验,验证仿真的正确性。结果表明：传动系统的激励主要集中在输入和输出平行轴齿轮处,传动系统和箱体振动的峰值频率均和齿轮啮合频率相近,变速箱箱体振动较大的位置位于箱体的底部。试验与仿真对比,试验中存在和仿真相近的峰值频率,仿真和试验所得的振动加速度均方根误差值小于20%,验证了仿真的正确性。     

风电机组齿轮箱故障分类方法研究

风电机组齿轮箱的运行工况复杂多变,很难获取大量的所有已知故障的样本数据,为了能够实现在无已知样本数据条件下的故障分类,提出了一种基于ART2神经网络和C-均值聚类算法的风电机组齿轮箱故障分类方法。首先利用ART2无监督神经网络实现样本数据的初步分类,再利用C-均值聚类算法对分类结果进行修正,克服了由于原始神经网络算法存在"硬竞争"导致分类精度下降的问题。分析结果表明提出的方法具有更高的分类准确度,能够对健康和不同故障类型的风电机组齿轮箱进行准确分类识别。     

A method for the compound fault diagnosis of gearboxes based on morphological component analysis

An improved morphological component analysis (MCA) method is proposed for the compound fault diagnosis of gearboxes. When gear fault and bearing fault occur simultaneously, the compound fault signal of the gearbox contains meshing components (related to the gear fault) and periodic impulse components (related to the bearing fault). The corresponding fault characteristics can be separated by MCA according to the morphological differences of the components. In the proposed method, the optimal dictionary, which can represent the characteristics of bearing faults, is first selected based on the principle of minimum information entropy. Then, the compound fault signal is decomposed into the meshing component and the periodic impulse component using MCA. Finally, the separated components are subjected to the Hilbert envelope spectrum analysis. The faults of the gear and the bearing can be diagnosed according to the envelope spectra of the separated fault signal components. Simulation and experimental studies validate the effectiveness of the proposed method for the compound fault diagnosis of gearboxes.     

基于LSTM-SVM的风电机组齿轮箱故障诊断

针对风电机组齿轮箱的故障诊断中特征提取过分依赖人为经验和准确率不高的问题,提出一种基于长短时记忆网络(LSTM)与支持向量机(SVM)相结合的方法。对原始时域振动信号作傅里叶变换,利用LSTM神经网络自适应智能提取特征的优势,结合SVM的分类功能,实现对风电机组齿轮箱更加准确的故障诊断。仿真结果显示,该网络模型在经过16轮训练后准确率可以达到100%,使用测试集数据准确率也可以达到99.1%。     

2t叉车液压泵齿轮箱传动轴装配同轴度的质保体系

说明确保 2t叉车液压齿轮箱和传动轴的装配同轴度的重要性 ,介绍车体架零件加工、整体焊接质保体系、传动机组加工装配质保体系及液压泵齿轮箱质保体系。     

中心传动齿轮箱体固有特性研究

齿轮箱体的固有特性 (固有频率、固有振型 )对整个系统振动和噪声有较大的影响。采用有限元法 ,建立了中心传动齿轮箱有限元动力学模型 ,用I -DEAS软件对箱体结构进行分析 ,计算出了齿轮箱的固有特性 ,试验结果与有限元计算结果基本吻合     

Describing the design space of mechanical computational design synthesis problems

An important challenge in mechatronic system design is to select a feasible system architecture that satisfies all requirements. This article describes (i) the necessary concepts that a system architect needs to be able to formally and declaratively describe the design space of mechanical design synthesis problems, thereby minimizing accidental complexity; (ii) how a Domain Specific Language based on the SysML modeling language and the Object Constraint Language (OCL) can be used to create this model of the design space; and (iii) an iterative process to come up with a formal model of the design space. This model describes the design space independent of any (knowledge of a) particular solving technology for the Design Space Exploration. Furthermore, the information in the model allows to select the most appropriate solving strategy for a particular design synthesis problem. The different concepts are illustrated on the example of automated synthesis of a gearbox.     

小波神经网络对风力发电机齿轮箱的故障诊断

为研究风力发电机组齿轮箱的故障特性,提高其工作的可靠性,提出采用小波神经网络对齿轮箱的故障进行诊断的方案。该方案采用小波包分析与径向基函数(RBF)神经网络相结合组成小波神经网络,以准确地识别风力发电机组中齿轮箱常见的故障。诊断结果证明了方案的可行性。该方案在风力发电机组齿轮箱故障诊断领域具有良好的实用前景。     

起重运输机械齿轮箱故障不解体诊断技术

指出齿轮箱故障的五种基本特征信号,阐述了这些信号特征与典型故障之间的的内在联系,为起重运输机械齿轮箱故障特征的提取与识别奠定了基础．