A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

A Comprehensive 3-Steps Methodology for Vibration-Based Fault Detection,Diagnosis and Localization in Rotating Machines

In any industry,it is the requirement to know whether the machine is healthy or not to operate machine further.If the machine is not healthy then what is the fault in the machine and then finally its location.The paper is proposing a 3-Steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance activity.The Step-1 identifies whether machine is healthy or faulty,then Step-2 detect the type of defect and finally its location in Step-3.This method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing defects.The method uses the optimised vibration parameters and a simple Artificial Neural Network(ANN)-based Machine Learning(ML)model from the earlier studies.The model is initially developed,tested and validated on an experimental rotating rig operating at a speed above 1st critical speed.The proposed method and model are then further validated at 2 different operating speeds,one below 1st critical speed and other above 2nd critical speed.The machine dynamics are expected to be significantly different at these speeds.This highlights the robustness of the proposed 3-Steps method.

Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm

The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.

A Study on the Levitation Control of the Multi-degrees-of-Freedom Rotational Machine Supported by Magnetic Bearings Using Flux Feedback

This paper deals with an open-loop characteristic of a magnetically levitated system including flux feedback. In order to design a controller to obtain a good disturbance rejection and to be insensitive to parameter variations, it might be useful to employ a flux feedback loop. The air gap flux which can be sensed by a proper sensor has linear relationship with respect to the change of the current and the air gap. This linear property decreases the inherent nonlinearity of the magnetic suspension system that is caused by the coupling between the electrical actuator and the mechanical plant. Simulation results achieved from a multi-degree-of-freedom numerical model show that the flux feedback loop makes an improvement of the performance of the magnetic suspension system against the load variations.

基于机器视觉的飞机故障检查系统

针对当前飞机维修检查工作以人工目视检查为主、效率低且存在人为因素影响的情况,设计一套基于图像识别与机器深度学习的飞机部件表面无损检测系统。收集并整理了某航空公司一线飞机维修员拍摄的飞机机身及发动机部件图片,对图片集进行预处理,包括通道提取、Sobel滤波处理及二值化;最后用Blob分析对处理后的图像进行特征提取与系统分析。系统运行速度快、准确率高且可连续自动识别图像。利用机器视觉技术对飞机部件表面进行无损检测不仅可以提高生产效率,同时可以去除人为因素对航空器飞行安全的影响,使得飞机的飞行安全得到进一步提升。实践证明,该系统性能稳定可靠,具有极高的推广应用价值。

基于旋量理论的镜像加工运动学建模及轨迹自动修调研究

针对镜像加工过程中结构变形致使加工精度难以控制的问题,建立了基于旋量理论的镜像加工剩余壁厚运动学模型,综合考虑零件加工要求及镜像约束关系,提出了镜像加工“支撑-铣削”轨迹生成方法,建立了针对工件变形补偿的镜像支撑轨迹修调模型,实现了镜像加工“支撑-铣削”轨迹自动修调。栅格壁板镜像加工验证试验结果表明,提出的镜像加工“支撑-铣削”轨迹生成和自动修调方法可以满足镜像加工剩余壁厚要求。

基于ALIF-MPE-SVM组合算法的电机轴承早期故障诊断

为了提高电机用轴承的安全运行稳定效率,通过ALIF算法自适应分解非平稳信号,再以MPE从IMFs中提取出非线性故障信号,将MPE降维处理后的故障特征量利用MPE-SVM思想智能故障的诊断功能,开发得到一种MPE-SVM故障诊断技术,再根据测试得到的电机轴承故障参数完成算法有效性验证。研究结果表明:大部分故障信息都出现于最初的三个IMF内,主成分比例超过80%,因此以前3个主成分作为特征量并将其代入MPE-SVM内实施训练。各组别都可以对故障损伤的准确识别,表明以MPE作为故障特征能够满足有效性要求。ALIF-MPE具备比EMD-MPE更优的分类性能,达到了较低的标准差,稳定的分类状态。该研究能够准确识别电机轴承不同故障程度,对提高同类机械传动设备的故障诊断水平具有很好的理论支撑意义。

基于双目视觉的堆叠零件识别与定位研究

针对堆叠零件识别定位耗时长与精度低等问题,设计了一种高效的识别定位方法。在识别方面,提出了一种分类器融合方法。通过使用此方法训练出一个权重可动态调整的分类器模型,成功实现了对堆叠零件的分类识别。在定位方面,采用改进随机Hough变换的方法提取目标边缘。利用堆叠零件的边缘几何特征,重建出堆叠零件的空间姿态信息。最后,对单个特征分类器和组合特征分类器进行了分类识别实验和模型评估。实验结果表明,特征融合后的分类器展现出更高的识别精度和更稳定的准确率波动,表明其具有更好的分类鲁棒性。

基于机器视觉的飞机电镀部件曲表面无损检测

[目的]为解决机器视觉在曲表面无损检测中算法复杂的问题,以及克服传统光源在曲表面检测中的局限性,开发了一套基于格栅光源的飞机电镀部件曲表面无损检测系统。[方法]该系统通过格栅光对电镀部件曲表面进行成像,随后进行图像分析,包括图像导入、预处理、Blob分析等步骤。[结果]该系统能够快速、准确地检测出电镀部件曲表面存在的缺陷,检测效率高且稳定可靠。[结论]该系统在飞机制造企业及航空公司飞机维护一线的初步工程应用中取得了良好效果,有望进一步推广。

面向类不均衡数据的多任务博弈概率分类向量机

在工程实际中获取的故障样本往往会呈现不均衡特点,同时传统的分类模型也会存在局限性。针对这些问题,基于稀疏贝叶斯理论、模糊隶属度等理论,提出了一种多任务博弈概率分类向量机(MGPCVM)分类方法。首先,在MGPCVM的目标函数中,设计了博弈因子,将不同类样本质心间的博弈信息赋予每个样本特定的样本质心敏感值,以解决传统分类器对不平衡数据集分类表现较差的问题;然后,在贝叶斯框架理论下,采用截断高斯先验分布的方法,使样本参数的正负与对应的标签信息相一致,且使样本质心敏感值产生了稀疏估计;最后,将MGPCVM方法应用于两种不同实验平台采集的滚动轴承实验数据处理,进行了故障诊断有效性验证。研究结果表明:在不同的不平衡比(IR)下,MGPCVM方法的准确率均保持在95%以上,相对于支持向量机(SVM)、概率分类向量机(PCVM)等方法提升了4%~8%;与典型向量式分类方法相比,MGPCVM方法可以在不平衡数据条件下表现出优越的分类性能,适用于实际工况中数据失衡的分类问题。

数控技术在农机零部件加工中的优化及应用

随着农业机械加工及制造行业的快速发展,为了满足农业机械多样化设计要求,数控节能加工技术通过精密的计算机控制,能够实现对农机零部件加工过程中各个参数的准确控制。针对传统农机零部件加工过程能耗大及难以控制的问题,基于带精英策略的非支配排序遗传算法NSGA-Ⅱ对加工工艺参数进行多目标优化,并进行实验对比分析。研究结果对于提高农机零部件加工精度、降低能耗、减少加工时间等提供技术参考与借鉴。

基于SSA-RVM的滚动轴承可靠度评估与预测

为解决滚动轴承运行可靠度预测问题,这里提出了基于麻雀搜索算法-相关向量机的滚动轴承可靠度预测方法。首先对轴承振动信号在时域、频域及时频域构成的维数较高的向量集利用主成分分析算法进行降维;然后将降维后的特征集作为轴承的退化状态特征输入到逻辑回归模型中,进行滚动轴承可靠性评估;然后将轴承的性能退化状态特征作为麻雀搜索算法-相关向量机模型的输入,获取预测结果;最终把结果带入到逻辑回归模型中,预测轴承的运行可靠度。实验结果表明提出的算法在预测滚动轴承运行可靠性中具有明显优势。

叶轮零件增减材复合加工工艺规划及虚拟仿真加工研究

针对叶轮零件结构复杂、加工难度大等问题,对其结构及工艺特点进行分析,制定增减材复合加工工艺规划,并基于NX软件设计增材和减材加工的刀具轨迹,最后通过在VERICUT中构建的虚拟仿真系统对加工过程进行仿真,验证增减材复合加工工艺及NC程序的正确性。虚拟仿真加工结果表明,该方法可为复杂零件高效、高质量加工提供一种新的思路和参考。

基于直驱机床的航空整体叶轮加工效率优化方法研究

为解决航空叶轮在复杂五轴联动加工过程中的效率和精度控制难题,本文基于直驱机床围绕加工参数对表面粗糙度和加工时间的影响进行了深入分析。首先通过与传统机床进行加工对比,证明了直驱机床的精度与速度方面的优势;其次针对原点坐标的研究揭示了原点位置对加工效率的影响,尤其是当原点接近A、C轴中心时,加工效率显著提高;最后通过正交试验法研究了转速、每齿进给量、切宽这3个关键因素,并发现每齿进给量是影响表面粗糙度的主导因素,其次是切宽,而转速的影响较小。在保证粗糙度小于1.6μm的前提下,以转速20000 r/mim、每齿进给量0.03 mm、切宽0.6 mm的参数组合获得最优结果。基于优化的工艺参数不仅提升了加工效率,节约了43.3%的加工时间,还确保了加工质量,为航空叶轮的精密加工提供了重要的技术参考,同时为基于直驱机床的复杂零件高效加工提供实践指导和理论依据。

风电轴承大尺寸钢球光球加工技术

针对国内现有风电轴承大尺寸钢球光球机加工精度低,加工效率低的问题,对钢球在光球机动、静磨盘沟道内的运动规律、受力特点和磨削机理进行分析,光球时钢球受到的磨削力与动、静磨盘沟道深度比有关,并基于ADAMS仿真得到最佳动、静磨盘沟道深度比为7∶3,去两极和环带、成形、走圆工序最佳动磨盘转速分别为90,120,90 r/min。样机试验结果表明,改进后光球机的光球效率提高了30%,球形误差至少提高了0.2 mm,表面粗糙度Ra值至少减小了0.03 mm。

基于自适应ROI的轴承内圈高精度视觉尺寸检测

针对机器视觉轴承内圈侧面复杂形状尺寸检测精度低的问题,提出根据检测目标建立小面积感兴趣区域(Region of Interest,ROI)的自适应选取方法和基于Zernike矩的ROI亚像素级边缘提取方法,大幅提升了轴承内圈尺寸的检测精度。首先分别拍摄轴承内圈左侧与右侧轮廓图像,对图像进行预处理。在此基础上,通过角点检测融合像素扫描的方法实现自适应ROI选取,解决了因轴承内圈移动引起的小面积ROI边缘误判问题;使用Canny算子提取ROI的像素级边缘,再用改进的Zernike矩算法得到亚像素级边缘。最后,分别对ROI中提取的边缘进行最小二乘圆拟合和直线拟合,根据像素当量与视场间隔将图像中各尺寸转换为轴承内圈实际尺寸。实验结果表明:所提方法测量的标准不确定度低于0.005 mm,满足轴承尺寸高精度检测的要求,对于实现轴承检测的自动化有实际意义。

某型复杂工程机械变速箱体复合工艺约束机加工线平衡研究

为解决机加工生产线平衡问题所包含的加工任务刀具需求、机床类型需求、加工方位约束、“紧密”型和“或”型约束等复杂条件,建立了机加工生产线平衡数学模型,并采用含多重筛选机制的粒子群算法进行了求解。首先,建立了满足此复杂实际约束条件的机加工生产线平衡问题数学模型;然后,采用粒子的位置坐标作为粒子群搜索的权重信息,进行了加工任务、集中任务的选取,并设计了多重筛选机制构造启发式任务集生成规则;采用此规则对待分配加工任务进行了多重筛选,以得到可供直接分配的加工任务集合,粒子群算法(PSO)从此集合中依次选取了加工任务,构成了完整的解,并形成了具体的任务分配方案;最后,为提高程序的实用性和可视性,设计了加工任务的甘特图生成模块,通过对某复杂工程机械变速箱体零件的实际案例研究,将简化后的任务信息代入算法进行了求解。研究结果表明:该方法实现了多组平衡率高于90%的优化结果,在节拍时间为1120 s时,得到94.66%的较高平衡率,排产方案表格内容与甘特图显示一致;算法推演结果满足设定的多种复合约束条件,通过与人工排产对比说明了该算法的有效性并具有较好的经济性、实用性;对柔性生产案例进行探讨,证明该算法运算结果具备一定的生产柔性。

基于不同加工工艺的微孔节流空气静压轴承的特性研究

随着超精密加工技术和地面微低重力仿真的发展,相关产业对空气静压轴承的性能要求越来越高。由于传统的孔式节流器其结构特点不能完全满足实际使用的需要,节流孔直径与气膜间隙同一数量级的微孔节流空气静压轴承因其良好的刚度和承载特性,越来越受到关注。但在微孔节流器的发展过程中,微孔的加工工艺却限制了其推广应用。针对目前常用的三类微孔加工工艺,进行了理论仿真研究,通过建立不同加工工艺轴承仿真模型,运用双向流固耦合仿真方法,对比分析了基于不同加工工艺的微孔节流空气静压轴承的动静态特性。研究结果表明:锥孔类轴承承载性能优于其他两类,但耗气量大且在小间隙时刚度较差;薄壁直孔类轴承承载性能稍逊,但在大间隙下轴承刚度较佳;嵌套类轴承承载性能较差,但在小间隙下轴承刚度较大且耗气量较低。