Vibration isolation performance analysis of a bilateral supported bio-inspired anti-vibration control system

To achieve better anti-vibration performance in a low frequency region and expand the range of vibration isolation,a bilateral supported bio-inspired anti-vibration(BBAV)structure composed of purely linear elements is proposed,inspired by the motion form of bird legs and the nonlinear extension and compression of muscles and tendons.The kinematic relations and nonlinear dynamic model considering vertical and rotational vibrations are established.The loading capacity and equivalent stiffness are investigated with key parameters.The amplitude-frequency characteristics and force transmissibility are used to evaluate the stability and anti-vibration performance with the effects of the excitation amplitude,rod length,installation angle,and spring stiffness.The results show that the loading requirements and resonant characteristics of the BBAV structure are adjustable,and superior vibration isolation performance can be achieved readily by tuning the parameters.The X-shaped vibration structure is sensitive to the spring stiffness,which exhibits a wider vibration isolation bandwidth with smaller spring stiffness.Besides,depending on the parameters,the nonlinear behavior of the BBAV system can be interconverted between the softening type and the hardening type.The theoretical analysis in this study demonstrates the advantages and effectiveness of the vibration isolation structure.

Research on Breeze Vibration Law and Modal Identification Method of Conductor Considering Anti-Vibration Hammer Damage

In the harsh environment,the structural health of the anti-vibration hammer,which suffers from the coupled effects of corrosion and fatigue damage,is significantly reduced.As part of the conductor structure,the anti-vibration hammer is rigidly attached to the conductor,effectively suppressing conductor vibration.The conductor’s breeze vibration law and natural modal frequency are altered damage to the anti-vibration hammer structure.Through built a vibration experiment platform to simulate multiple faults such as anti-vibration hammer head drop off and position slippage,which to obtained the vibration acceleration signal of the conductor.The acceleration vibration signal is processed and analyzed in the time and frequency domains.The results are used to derive the breeze vibration law of the conductor under multiple faults and propose an anti-vibration hammer damage online monitoring technology.The results show that the vibration acceleration value and vibration intensity of the conductor are significantly increased after the anti-vibration hammer damage.The natural frequency increases for each order,with an absolute change ranging from 0.15 to 6.49 Hz.The anti-vibration hammer slipped due to a loose connection,the 1st natural frequency increases from 8.18 to 16.62 Hz.Therefore,in engineering applications,there can be no contact to determine the anti-vibration hammer damage situation by monitoring the modal natural frequency of the conductor.This is even a tiny damage that cannot be seen.This method will prevent the further expansion of the damage that can cause accidents.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Research on Track Fastener Service Status Detection Based on Improved Yolov4 Model

As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.

Corrosion Test of the Steel Plate in a WJ-8 Fastener for High Speed Rail

It was found that the steel plate in the composite plate in the WJ-8 fastener used in high speed rail is rusty. The objective of this study is to test the zinc coating of the steel plate. A literature review was conducted to identify the zinc coating techniques, and the companies that can provide different coating service was identified. A salt fog chamber was built that was in compliance with the ANSI B117 code, and the steel plates that were coated by the identified companies were tested using the salt fog chamber. The results indicated that the coating technique that had the best performance in preventing corrosion was the Greenkote plates with passivation. The galvanized option had the roughest coating layer, and it was the most reactive in the salt water solution. This makes it non-ideal for the dynamic rail environment because the increased friction of the plate could damage the supports, especially during extreme temperatures that would cause the rail to expand or contract. Greenkote with Phosphate and ArmorGalv also provided increased corrosion prevention with a smooth, strong finish, but it had more rust on the surface area than the Greenkote with ELU passivation. The ArmorGalv sample had more rust on the surface area than the Greenkote samples. This may not be a weakness in the ArmorGalv process;rather, it likely was the result of this particular sample not having the added protection of a colored coating.

Rail fastener detection of heavy railway based on deep learning

Image detection based on machine learning and deep learning currently has a good application prospect for railway fault diagnosis,with good performance in feature extraction and the accuracy of image localization and good classification results.To improve the speed of locating small target objects of fasteners,the YOLOv5 framework model with faster algorithm speed is selected.To improve the classification accuracy of fasteners,YOLOv5-based heavy-duty railway rail fastener detection is proposed.The anchor size is modified on the original basis to improve the attention to small targets of fasteners.The CBAM(Convolutional Block Attention Module)module and TPH(Transformer Prediction Head)module are introduced to improve the speed and accuracy issues.The rail fasteners are divided into 6 categories.Experiment comparisons show that before the improvement,the MAP@0.5 value of all categories are close to the peak of 0.989 after the epoch of 150,and the F1 score approaches 1 with confidence in the interval(0.2,0.95).The improved mAP@0.5 value approached the highest value of 0.991 after the epoch of 75,and the F1 score approached 1 with confidence in the interval(0.01,0.95).The experiment results indicate that the improved YOLOv5 model proposed in this paper is more suitable for the task of detecting rail fasteners.

某线震后轨道设计方案研究

2022年1月8日门源发生6.9级地震,造成某线隧道二衬坍塌、桥梁梁体移位、接触网脱落等基础、设备严重损坏,并伴随钢轨折断、扣件脱落、道床倾斜等轨道震害,是我国高速铁路首次遭遇的强震作用下严重受灾事件。结合震后预测余滑变形量水平150~300 mm,垂向100 mm条件下,在设防区段提出一种“三孔连体套管承轨台可调式WJ-8型扣件+长枕埋入式单层道床预留切割孔”新型大调整量无砟轨道结构方案。其中,三孔连体套管承轨台可调式WJ-8型扣件在既有WJ-8型扣件基础上通过增设铁承轨台、调距块和三联套管等部件可满足单股钢轨左右位置调整量达152 mm,长枕埋入式单层道床中预留切割孔,便于在基础变形超出扣件调整范围后切割纠偏;通过对余滑变形后线路拟合获得拨距包络图,确定了大调整量无砟轨道铺设范围。

杭州世纪中心西天窗索夹抗滑移试验研究

索夹是预应力索结构中拉索与其他构件连接的重要节点构件,若索夹和拉索之间发生滑移,会产生较大的预应力损失,甚至改变结构的受力性能。杭州世纪中心西天窗采用箱形桁架+钢索结构,其结构特殊性决定了拉索在施工张拉和施工完成后的状态不同,即施工张拉时要求拉索对索夹是可相对滑移的,而施工完成后需要锁定拉索和索夹的相对位置,不允许发生滑移。通过拉索施工张拉、高强螺栓紧固力衰减、索夹顶推三个阶段的全过程模拟试验确定了索夹与拉索之间的摩擦力情况。试验结果表明,在拉索施工张拉阶段,索夹对索体产生的摩擦力较小,通过锌板垫层能够有效降低铸钢索夹节点处的摩擦损失,保证拉索张拉过程中索力的有效传递;高强螺栓终拧后12h内会产生不可忽视的紧固力损失;使用阶段索夹的抗滑移承载能力能够确保索夹节点安全。

扣件系统横向刚度及抗倾翻试验研究:以WJ-8型扣件为例

为了对扣件系统横向刚度及抗倾翻性能进行研究,首先借鉴等间隔弹性点支承梁模型分析钢轨横向力学行为;然后,考虑钢轨自身截面特性,推导扣件系统横向刚度及抗倾翻系数计算公式;最后,设计并进行1组及3组WJ-8型扣件横向加载试验,验证分析及推导的合理性。试验结果表明:当钢轨整体扭转中心为轨底中心点时,钢轨自身扭转不可忽略,钢轨横向力及扭矩的传递过程都是非线性过程;1组及3组扣件试验得到的位移相对误差不超过6.8%,转角相对误差不超过9.5%;3组扣件试验允许加载范围更大,且更适用于非对称式扣件;WJ-8型扣件钢轨横移过程可按扣件横向反力的组成划分为钢轨滑移、线性变形和非线性变形3个阶段,钢轨倾翻过程可按钢轨轨底与轨下垫板接触形式划分为钢轨脱离和整体倾翻2个阶段。扣件横向刚度及抗倾翻系数可通过横向力-位移曲线、扭矩-转角曲线确定。

基于3D线激光传感器的轨道弹条扣件结构缺陷检测方法

轨道扣件缺陷是铁路安全运行的重大安全隐患,基于二维图像处理方法能检测扣件外观缺陷,但难以检测扣件结构缺陷,提出了一种3D线激光传感器的轨道扣件结构缺陷检测方法。首先,利用3D线激光传感器获取轨道点云,根据扣件高度快速定位扣件区域点云,利用PointNet++网络对扣件区域点云分割获得弹条点云;其次,将弹条点云映射至二维图像,在二维图像中提取弹条骨架,二维骨架融合至三维点云获得初始骨架,对每个初始骨架点云拟合截面圆,以各截面圆心作为弹条骨架精确表示弹条轮廓及空间结构;最后,提取弹条三维骨架的特征点,根据特征点构造扣压平面和计算弹条缝隙,基于弹条缝隙检测扣件结构缺陷。为了验证文中方法的有效性,以WJ-7、WJ-8、WJ-2型弹条扣件为对象测量弹条缝隙,并将文中方法测量的弹条缝隙与人工使用缝隙尺测量的真实值进行比较,单个扣件的测量误差在0.1 mm内,且文中方法对轨道油污、锈斑及环境有较好的鲁棒性;对批量扣件的结构缺陷检测,当测量误差允许在±0.1 mm时,扣件结构缺陷检测的准确率不低于95%。

重型燃气轮机拉杆组合转子热瞬变振动特性研究

重型燃气轮机冷态启动的稳速升负荷阶段,常出现轮盘间径向变形不协调导致的相对滑移,拉杆组合转子会发生振动随燃气轮机转子温度的增加而显著变化的热瞬变振动现象。建立了周向拉杆组合转子平面摩擦带止口配合结构轮盘相对滑移的力学模型,包括平面摩擦、周向拉杆和止口与轮盘相对滑移的力学模型。以某型重型燃气轮机拉杆组合转子为研究对象,对其冷态启动过程中出现的热瞬变振动进行了分析,根据轮盘相对滑移的规律,通过减小轮盘间的变形不协调,并适当增加止口配合的过盈量的方法,成功优化了该重型燃气轮机拉杆组合转子冷态启动过程中的热瞬变振动。

地铁扣件刚度对车致轨道-隧道振动影响分析

为揭示减振扣件动力性能参数对轨道-隧道振动特性的影响规律,对减振扣件轨道与一般非减振轨道的动力响应进行现场实测,对比分析两种类型轨道的钢轨、道床与隧道壁的振动特性,同时建立车辆-轨道耦合动力学仿真模型与轨道-隧道-大地有限元模型,进行不同扣件刚度下钢轨加速度导纳、轨道振动衰减率与隧道动力响应的影响分析。对比线上实测结果发现,减振扣件轨道中钢轨、道床与隧道壁的振动均明显减弱;减小扣件刚度对车体的振动响应无显著影响,钢轨的位移与加速度会随之增大;减小扣件刚度会增强钢轨的低频振动,但会减弱钢轨的高频振动;沿钢轨纵向1~50Hz频段的振动耗散更为迅速,500~1000Hz频段的振动更容易沿着钢轨纵向传播;减振扣件对50~200Hz频段隧道动力响应有明显影响,能有效降低隧道壁测点处振动响应6~8dB。

新型有轨电车扣件系统设计及性能

针对国内有轨电车无枕式扣件调高量不足、共享路权地段扣件强度尚待加强等问题,提出了一种新型有轨电车扣件系统,并对该扣件进行了有限元分析和室内试验。该扣件系统为弹性不分开式有挡肩扣件,其中基板和调高座采用玻纤增强聚酰胺材料注塑成型,在保证承载力的前提下提高了基板和调高座的防腐蚀性和绝缘减振性;其余零部件为国铁标准件或地铁通用件。该扣件垂向调整量为0~+20 mm,轨距调整量为-12~+8 mm,调整极差为1 mm。计算和试验结果表明:本文研发的新型有轨电车扣件系统关键零部件受力和变形满足要求,结构可靠;300万次荷载循环后各零部件无损伤,无失效;能满足有轨电车对扣件系统调整量、强度和可靠性的要求,性能稳定,维修养护方便。将该扣件系统应用于实际工程,通车运营三年,服役状态良好。

基于改进YOLO_v3算法的车辆轮胎紧固件检测方法优化

轮胎拆装是汽修行业修补轮胎必要步骤之一,由于重型货车轮胎过重导致工人腰椎受到严重损伤。现将EfficientNet-B4轻量型网络结构算法替换YOLO_v3算法的主干部分(DarkNet-53),从而实现各类车型轮胎紧固件的识别。替换后的网络参数大量减少,紧固件样本训练速度加快。YOLO_v3使用二分类交叉熵损失函数对正负样本分类并计算损失,现使用Focal loss分类损失函数替换二分类交叉熵损失函数,从而提出一种新的神经网络模型(YOLO_v3-Nut)。实验结果表明,YOLO_v3-Nut模型在训练与识别速度特性上都更优于YOLO_v3模型,且文中的模型结构比YOLO_v3模型储存空间减少了43.01%,算法平均准确率(MAP)为93.2%,同时检测速度为36 fps,足够完成各类车型轮胎紧固件的识别。

多重载荷下螺接脆性塑料零件的强度设计校核

针对脆性塑料零件采用螺纹联接安装时,在各种工况环境的静载荷和动载荷输人条件下,对安装孔区域出现裂纹的失效模式进行了分析。通过理论计算和结构仿真分析相结合,提出了强度计算校核方法和安装紧固力矩限值的计算方法,并通过力学环境试验进行了有效验证,给出了安装紧固的改进设计措施。

32 m T型梁梁端变形对无砟轨道扣件受力规律研究

无砟轨道扣件实现了钢轨与混凝土道床、下部基础的连接。扣件受力除轮轨作用下的正常服役工作状态外,还包括桥梁地段梁端变形诱发扣件受力状态变化。为保障桥梁地段无砟轨道扣件正常服役状态,本文以32 m T型梁为载体,通过分析梁端转角、垂向位移、预留梁缝及扣件安装位置等变量,研究了T型梁梁端变形及接口构造对无砟轨道扣件受力的影响。结果表明:梁端变形引起的扣件上拔力与梁端转角、垂向位移及转角位移组合变形线性相关,提出了不同构造变量与扣件上拔力的线性表达式。研究成果可为客货共线桥梁铺设无砟轨道梁端变形控制及构造设计提供参考。

基于3D相机的轨道扣件部件丢失与松动智能检测

轨道扣件在运营过程中会出现松动甚至掉落、断裂等异常情况,不利于列车行驶稳定和安全,需要进行定期、及时的检查与维修。传统的人工巡检效率低,难以匹配我国轨道交通的快速发展,且对于部件松动等不易察觉的问题检测效果差。利用计算机视觉形成自动化的检测设备逐渐成为发展趋势,其中基于三角测量原理的线结构光技术因其成本低、精度高、速度快等优点得到广泛应用,且适合轨道检测场景。该技术核心设备为可以采集并分析线结构光进行三维重建的3D相机,基于成像原理设计可搭载于轨道检测车的扣件检测系统并进行现场试验,经过数据分析和处理可以分别得到高质量的图像数据和三维模型。针对图像数据利用目标检测的方法,构建数据集,搭载YOLO(You Only Look Once)v5深度学习模型,实现挡肩及扣件部件的快速识别,进行部件丢失检测;针对三维模型利用轨道扣件相对位置固定的特点,根据阈值筛选扣件数据并进一步得到弹条及螺栓等部件的坐标信息,通过边缘提取、平面拟合等方法计算位移量,进行部件松动检测。研究结果表明,检测系统可以采集高质量的扣件数据,扣件部件识别平均精准度达到99.0%,速度满足现场实时检测的要求,同时对于弹条和螺栓的松动量检测精度分别达到了1 mm和0.1 mm。该方法具有实际工程价值,可以大幅提升轨道巡检效率,对于扣件部件丢失、松动等严重问题可以及时预警指导修复,保障轨道安全服役性能。

基于机器学习的直角扣件滑移和扭转性能预测方法

直角扣件初始刚度对模板支架结构承载力有较大影响,但扣件性能的量化设计存在工程量大、计算精确度低等问题.基于一系列扣件抗滑移和扭转试验,建立带直角扣件节点的三维数值模拟并利用试验结果进行验证,通过验证后的数值方法开展大量参数分析揭示多种设计参数对扣件性能的综合影响并建立扣件承载力设计数据库,分别基于随机森林(RF)、支持向量机(SVM)和K最邻近算法(K-NN)提出了扣件刚度预测模型,结合基因表达式编程(GEP)提出了抗滑移模型测点位移和扭转模型刚度预测表达式.研究表明,通过SVM和GEP能够较准确预测直角扣件抗滑移模型位移和扭转模型刚度,对指导工程模板支架结构中扣件的安全设计有着重要意义.

语义数据标注的轻量化轨道扣件故障检测方法

针对常规深度学习网络规模大、对现场设备硬件要求高且人工标注位置数据复杂费时等问题,提出了一种语义数据标注的轻量化轨道扣件故障检测方法。该方法仅对训练数据做语义标注,改进轻量化Transformer模型,嵌入梯度加权类激活映射(gradient-weighted class activation mapping,简称Grad-CAM)模块对模型输出的特征图权重作映射处理,可将模型对轨道扣件检测效果可视化。将获得的激活图进行二值化定位检测目标位置,实验结果表明,在真实铁路环境下,改进的轻量化轨道扣件模型的准确率为94.31%。

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某300MW亚临界汽轮机运行期间出现调节级压力升高17%~20%、轴向位移增大等异常情况,无法满足机组满负荷运行要求。揭缸检查发现高压第二级隔板外导流环与隔板连接的连接螺栓全部断裂,脱落的导流环堵塞隔板进汽通道,通流面积减小导致调节级压力升高。通过宏观形貌观察、化学成分、金相检验以及运行工况等分析导流环螺栓断裂原因,结果表明:螺栓为金属疲劳断裂;螺栓材质在部分工况下无法满足机组运行期间调节级后最高温度要求,使用温度裕度偏低;运行期间隔板变形将对螺栓产生剪切力。为避免此类型机组再次出现导流环脱落问题,提出了相应的建议。