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%。     

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。     

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.     

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.     

Benefits and limitations of parametric design implementation in helicopter gearbox design phase

This paper describes a parametric design methodology implemented to reduce gearbox design phase. A helicopter gearbox is a complex mechanical system, in which parts are heavily loaded in order to save weight but that have to run for thousands of hours without failure. Such a system is strongly optimized therefore the smallest change in specifications or in geometrical environments can drastically jeopardize the scheduling. In order to speed up the gearbox design process and to strengthen its robustness regarding specifications variability, a parametric design methodology has been developed thanks to a close collaboration between the laboratories and Eurocopter.     

DC16J200T汽车变速器设计特点剖析

介绍了根据国内外重型汽车变速器的发展方向,采用DFMEA失效模式分析方法、关键部件先期试验、计算机模拟仿真等多项国际先进设计技术,解决了重型汽车变速器行星机构后副箱设计、换挡机构可调节定位设计、超薄大直径转子泵及辅助喷淋强制润滑系统设计、三段式多挡位变速器电控单元TCU设计等多项技术难题,实现了多挡位、大转矩重型汽车变...     

基于迭代的集总经验模式分解算法的齿轮箱故障特征提取

针对集总经验模式分解方法(Ensemble Empirical Mode Decomposition,EEMD)在实际应用中存在的盲目添加白噪声的问题,提出了一种迭代的集总经验模式分解方法(Iterative Ensemble Empirical Mode Decomposition,IEEMD).首先介绍了IEEMD...     

大型风电机组传动系统动力学仿真分析

针对国产750 kW风力发电机组传动系统故障率较高的特点及增速箱齿轮的失效形式,采用计算机辅助工程技术,重新设计增速箱的传动结构;建立传动系统动力学模型并进行动力学仿真,得到啮合齿轮间接触力与接触区域随时间变化的数据,为进一步的结构有限元分析和优化提供参考。另外,在工作条件恶劣的情况下,对风电机组进行冲击载荷下的动力学仿真分析,得出系统结构的力与力矩的突变曲线图,为风电机组各部件结构及控制系统的设计提供数据基础,为进一步了解风电机组设计机理打下基础。     

A data-driven approach to diagnostics of repetitive processes in the distribution domain – Applications to gearbox diagnostics in industrial robots and rotating machines

This paper presents a data-driven approach to diagnostics of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against an available nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback–Leibler distance. To decrease sensitivity to disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The approach is demonstrated with successful experimental and simulation applications to wear diagnostics in an industrial robot gearbox and for diagnostics of gear faults in a rotating machine.