Vibration signal-based chatter identification for milling of thin-walled structure

In high speed milling aeronautical part, tool condition monitoring(TCM) is very important, because it is prone to get a chatter owing to the low stiffness of thin-walled structures. And the TCM is key technology for automated machining. In this paper, aiming to chatter monitoring in thin-walled structure milling, a variational mode decomposition – energy distribution(VMD-ED)method is proposed to improve the identification accuracy. And a moving average root mean square – mean value(MARMS-MV) ide...     

基于长短期记忆网络时序数据趋势预测及应用

为了研究状态监测大数据对设备运行状态的估计和预测,提出了一种人工经验与主成分分析相结合的长短期记忆网络方法(AEPCA-LSTM),利用运行过程中的监测时序数据对设备运行趋势进行预测.通过基于人工经验的主要成分分析方法(AEPCA)从状态监测系统中提取与目标变量最相关的状态变量作为输入;利用长短期记忆网络(LSTM)对...     

飞艇骨架结构动态损伤识别方法

针对飞艇骨架结构中损伤引起的模态跃迁现象导致无法通过匹配损伤前后动态特性参数变化来识别损伤的难题,给出3种只基于损伤后振动响应信息进行损伤识别的动态方法。通过模态分析方法获得结构的模态参数,分别推导模态振型曲率法、均布载荷面曲率法和虚拟轴向应变法等3种损伤识别算法。定义损伤指标,并根据损伤指标局部峰值来识别和定位损伤杆件。以半硬式飞艇常见狭长构型三角截面碳纤维复合材料桁架为例,结合有限元法和自编MATLAB程序进行损伤识别仿真研究,影响参数包括损伤类型、损伤位置、损伤程度和噪声量级等,最后对损伤识别算法的有效性进行试验验证。结果表明新损伤识别方法对损伤敏感,在环境噪声工况下能准确识别和定位单个和多个损伤杆件。文中方法均基于结构整体振动信息进行损伤杆件识别,将来可用于构造飞艇骨架实时结构健康监测系统。     

考虑连接非线性的大型桁架天线分散协调控制

针对含非线性连接的大型桁架式天线,考虑铰链非线性所产生的影响,基于连接子结构模态综合建模方法,建立其低阶非线性动力学模型。在此基础上,将动力学模型转换为分散参数化模型,并考虑状态变量不完全可测因素,设计适用于一致性理论的最优观测器。然后基于图论的思想提出桁架式天线的一致性形面保持控制方法,不仅实现了桁架式天线的高精度形面保持控制,同时对作动执行机构的失效具有容错性。仿真结果表明了所提控制方法的有效性。     

变截面旋转裂纹梁横向振动特性的研究

对损伤结构进行动力特性分析是进行无损检测的重要基础,而对于旋转梁结构的裂纹损伤动力特性研究,却鲜有文献涉及。以变截面旋转裂纹梁为研究对象,对其横向振动特性进行研究,提出一种求解变截面旋转裂纹梁横向振动特性的新方法。首先利用扭转弹簧模拟裂纹效应,建立含裂纹梁局部柔度模型,然后采用Frobenius方法求解振动方程,得到方程的级数解析解,并研究裂纹位置和深度对振动频率的影响,分析不同损伤程度、不同转速工况下梁的前两阶固有频率变化情况。结果表明:本文方法是有效的,转速和损伤程度的变化并非独立影响梁的固有频率,两者间具有耦合作用机理,对于变截面梁同样成立。     

基于 LSTM的飞控系统状态监控

监测飞控系统状态参数是保证无人机飞行安全的重要手段。针对无人机飞控系统的组成特点和飞行控制律,设计并构建了基于长短期记忆网络(Long Short Term Memory Network,LSTM)的飞控系统状态监控模型。首先,利用无人机历史飞参数据训练模型,建立输入飞参数据与状态参数的回归映射关系;然后,利用训练好的网络模型,实时预测飞控系统的状态参数,通过对比实测值与预测值之间的差异,实现飞控系统的状态监控。选取无人机飞参数据进行实验,基于 LSTM的算法比反向传播神经网络(BPNN)、支持向量机(SVM)预测精度高,MSE平均值分别低 0.01和 0.26,MAE平均值分别低 0.05和 0.12。结果表明,所提出的方法能够有效监控飞控系统,为无人机飞行管理决策提供数据支持。     

Trajectory prediction in pipeline form for intercepting hypersonic gliding vehicles based on LSTM

The interception problem of Hypersonic Gliding Vehicles (HGVs) has been an important aspect of missile defense systems. In order to provide interceptors with accurate information of target trajectory, a model based on an improved Long Short-Time Memory (LSTM) network for trajectory prediction pipeline is proposed for the interception of a skip gliding hypersonic target. Firstly, for trajectory prediction required by intercepting guidance laws, the altitude, velocity and velocity direction of the target are formulated in the form of analytic functions, consisting of linear decay terms and amplitude decay sinusoidal terms. Then, the dynamic characteristics of the model parameters are analyzed, and the target trajectory prediction pipeline is proposed with the prediction error considered. Finally, an improved LSTM network is designed to estimate parameters in a dynamically-updated manner, and estimation results are used for the calculation of the final trajectory prediction pipeline. The proposed prediction algorithm provides information on the velocity vector for midcourse guidance with the effect of prediction errors on interception taken into account. Simulation is conducted and the results show the high accuracy of the algorithm in HGVs’ trajectory prediction which is conducive to increasing the interception success rate.     

基于深度学习的滑油监测方法研究

针对传统的数据特征提取方法难以提取航空发动机滑油监测数据有效特征的缺陷,提出了一种基于多尺度卷积神经网络(Multi-scales convolutional neural network,MSCNN)、长短期记忆(Long short-term memory,LSTM)神经网络和BP网络的单通道网络模型MSCNN-LSTM-BP。将多尺度学习融入CNN,MSCNN和LSTM以串行方式提取数据在空间维度和时间维度的二维特征。实验结果表明:3尺度的MSCNN-LSTM-BP对数据样本的分类准确率达到98.2%,单组电容数据采集测试时间仅为2.1986ms,综合分类率F1达到98.57%,总体性能优于CNN,LSTM和传统的多尺度特征提取方法。MSCNN-LSTM-BP满足航空发动机滑油监测对于实时性和准确性的要求,具有良好的适用性。     

基于LSTM和CNN的高速柱塞泵故障诊断

针对高速轴向柱塞泵容易发生空化，且目前空化故障诊断方法存在依赖手工特征提取、鲁棒性不高的问题，提出了一种基于长短时记忆（LSTM）和一维卷积神经网络（1D-CNN）相结合的空化故障诊断方法。搭建了柱塞泵故障实验台，采集柱塞泵在不同空化等级下的壳体振动信号。利用LSTM和1D-CNN搭建的分类模型对不同进口压力情况下的振动信号进行空化等级识别。实验结果表明：提出的方法能够准确地识别出4类不同的空化等级，准确率高达99.5%，同时在不附加降噪方法的情况下，具有良好的鲁棒性，在0 dB信噪比的情况下，识别准确率高达87.3%。     

1000kV特高压钢管构架雷暴冲击风致振动响应研究

1000kV钢管构架属于风敏感结构,风与结构的相互作用十分复杂,风荷载常常是设计的主要控制荷载。本文以某一特高压钢管构架为工程背景,详细研究了冲击风风场的数值模拟方法,应用Wood竖直风剖面方程与Holmes经验模型模拟平均风场,以及使用稳态高斯随机过程模拟冲击风脉动风场,使得模拟的风场与实际的雷暴冲击风较为一致。利用精确的有限元模型,获得了结构的自振动力特性,在时域内得到了构架风致振动响应时程。研究了结构位移平均值、位移均方根值和加速度均方根值的分布特点,同时计算比较了不同风场时构架典型节点的风振系数。通过研究,揭示了1000kV钢管构架的风致振动特性,结果可作为构架结构抗风设计的参考。     

刚性模型风洞试验确定大跨屋盖结构风振系数的多阶模态力法

风流经过大跨屋面时,由于气流分离在屋面的大部分区域产生强大的吸力,并引起柔性屋面结构的振动,因此大跨屋面结构抗风设计需考虑风振响应和风振系数.本文根据振型迭加原理,提出了利用刚性模型风洞试验确定大跨度柔性屋盖结构风振响应和风振系数的多阶模态力法,并推导了风振响应和风振系数的计算公式.该方法可以考虑高阶振型的贡献.通过与气动弹性模型风洞试验结果以及直接时程法相对比,发现多阶模态力法能够得出令人满意的结果.     

振动特性敏感区上的结构故障诊断与监测

本文提出了通过敏感的振动特性来进行结构故障早期诊断的图象识别诊断法和参数变化诊断法。前者可以判断故障是否发生并作简单分类,后者能同时确定故障的部位和严重程度。两方法都用到了悬臂梁的故障诊断中,结果表明是有效的。     

钝体涡激力参数的识别

涡激振是柔性结构易发生的气动弹性现象。本文通过风洞试验，研究了均匀流场中弹性支承的二维刚性模型的强激力，提出了一种识别涡激力模型中未知参数的方法。用本文的方法，进行了三种钝体模型的风洞试验。     

结构动特性分析的超单元法

提出一种超单元法,将一个细化的静力学有限元模型转化为反映整体性能的动力学有限元模型。这里定义的超单元具有子结构和协调有限元双重特性。生成了刚性超结点和弹性超结点,以及3刚性超结点和4刚性超结点超单元,从而为构造超单元模型奠定了基础,实现了建模的一致性。算例表明本文提出的超单元法是对动力学有限元模型的精确描述,算法的计算效率高,工程适用性好。     

灾害天气下大跨屋盖多模态随机离散风致响应研究

研究了具有空间相关性随机风荷载有色噪声过程的数学模型,讨论了用随机白噪声过程滤波生成脉动风荷载的方法。高阶振型对大跨屋盖随机风致响应的贡献不可忽略。本文根据模态解耦原理并结合精细时程积分法,提出了多模态随机离散精细积分法,推导了大跨屋盖风致响应与风效应系数的计算公式。通过大跨屋盖刚性和气弹模型风洞试验,计算了屋盖随机风致动力响应时程。同时研究了大跨屋盖的荷载风效应系数和位移风效应系数的分布情况,结果与有限元分析较吻合,验证了多模态随机离散精细积分法的正确性,证实了该方法是一种简便、快速、准确的大跨屋盖风致响应计算方法。     

涡轴发动机动力涡轮转子动力特性研究

应用有限元方法建立了某新型涡轴发动机动力涡轮转子动力特性的计算模型,对转子在不同情况下的动力特性——临界转速、振型和不平衡响应分别进行了系统的计算分析,得到了转子的前5阶临界转速、前5阶主振型和前两阶不平衡响应曲线,并计算了平衡卡箍对转子动力特性的影响。试验表明:计算模型能真实反映转子的动力特性。研究结果为转子的后续高速动平衡试验提供了参考和依据。      

Multi-probe linear fitting and time of arrival linear correction method to analyze blade vibration based on blade tip timing without once-per-revolution

Blade vibration monitoring can ensure the safe operation of aero-engine rotor blades. Among the methods of blade vibration monitoring, Blade Tip Timing (BTT) method has attracted more and more attention because of its advantages of non-contact measurement. However, it is difficult to install the Once-Per-Revolution (OPR) probe in the confined space of aero-engine, and the failure and instability of the OPR signal will reduce the reliability of the blade vibration analysis results, which directly affects the accuracy of the blade vibration parameters identification. The Multi-Probe linear fitting and Time of Arrival (ToA) Linear Correction method based on the BTT (MP-LC-BTT) without OPR is proposed to reduce the errors of single probe linear fitting method for blade vibration displacement analysis. The proposed method can also correct the calculation error of blade vibration displacement due to the nonlinear change of rotation speed, which can improve the analysis accuracy of the blade vibration displacement. A new blade vibration model conforming to the actual vibration characteristics is established, and the effectiveness of the proposed method is verified by numerical simulation. Finally, the reliability and accuracy of the MP-LC-BTT method have been verified by the experiments which include two high-speed blade test-benches and an industrial axial fan. This method can be used in the actual aero-engine monitoring instead of the BTT method with OPR.     

Boundary condition modelling and identification for cantilever-like structures using natural frequencies

The actual boundary conditions of cantilever-like structures might be non-ideally clamped in engineering practice, and they can also vary with time due to damage or aging. Precise modelling of boundary conditions, in which both the boundary stiffness and the boundary mass should be modelled correctly, might be one of the most significant aspects in dynamic analysis and testing for such structures. However, only the boundary stiffness was considered in the most existing methods. In this paper, a boundary condition modelling and identification method for cantilever-like structures is proposed to precisely model both the boundary stiffness and the boundary mass using sensitivity analysis of natural frequencies. The boundary conditions of a cantilever-like structure can be parameterized by constant mass, constant rotational inertia,constant translational stiffness, and constant rotational stiffness. The relationship between natural frequencies and boundary parameters is deduced according to the vibration equation for the lateral vibration of a non-uniform beam. Then, an iterative identification formulation is established using the sensitivity analysis of natural frequencies with respect to the boundary parameters. The regularization technique is also used to solve the potential ill-posed problem in the identification procedure.Numerical simulations and experiments are performed to validate the feasibility and accuracy of the proposed method. Results show that the proposed method can be utilized to precisely model the boundary parameters of a cantilever-like structure.     

燃油管路系统振动特性有限元技术研究

航空发动机燃油管主要用于提供燃油,属于复杂3维管路系统.为了更好地分析管路系统的振动特性,以双路总管燃油管路系统为例,结合其管路繁多、连接结构多样、支撑结构特殊、管内充液等特点,系统研究了各部件的有限元模拟方法.考虑到支撑刚度对管路系统振动特性有较大影响,采用有限元静力分析对其进行了求解.结合以上不同部件的分析方法,建立起系统的振动有限元模型.对该管路系统进行模态分析,得到系统固有频率变化规律及具有分簇特点的振动模态;对系统进行共振评估,认为副燃油分管最有可能发生共振,提出了振动优化的合理建议.     

Investigation on nonlinear lateral-torsional coupled vibration of a rotor system with substantial unbalance

Substantial unbalance may be caused by fan blade off during the operation period of gas turbine engines, and related dynamic problems are very critical to the safety design of rotor system in aero-engine. This article aims to understand lateral-torsional coupled vibration of the rotor system with substantial unbalance. The governing equation of a modified unbalanced rotor system is established based on Lagrangian approach. Then, a mathematical analytical method is proposed in which a linear appr...