SS_(4G)型电力机车车轮轮心裂损的检测

根据SS4G型电力机车铸钢车轮轮心和整体辗钢车轮裂损情况,分析裂损原因,介绍目视检查、磁粉探伤、超声波探伤及脉冲涡流探伤的裂损检测方法。结合实际,提出减少轮心裂损发生的措施和出现裂损时的应对方法。     

DF_(4B)型机车车轴齿端轮座内侧超声波探伤波形分析

探讨超声检测DF4B型机车车轴齿端轮座内侧各种反射回波的形成原因,分析组装间隙、组装应力和疲劳裂纹反射回波波形特点及其区别,针对企业标准中关于缺陷的评定要求提出修改意见。     

鲁棒实时钢轨表面擦伤检测算法研究

应用图像处理和模式识别技术,分析高速线阵扫描相机采集的钢轨数字图像,提出鲁棒实时的钢轨表面擦伤检测算法。这种算法首先分析采集图像在垂直方向的投影曲线,提取准确钢轨图像;之后,借鉴人类视觉对比度感知机理,将钢轨灰度图转换为灰度对比图,并基于最大熵原理进行二值化处理,分割出可疑擦伤区域;然后根据经验知识判定钢轨表面的可疑擦伤。实验验证表明:新算法的检测性能高,平均准确率为90.7%,平均漏检率为3.95%;检测速度快,平均检测时间不超过40 ms。     

双曲拱桥的病害检测及加固措施

结合某双曲拱桥外观检查结果对其病害产生原因进行分析总结,提出加固措施。     

公路隧道病害分析及健康检测

简要分析了目前公路隧道的常见病害,介绍了公路隧道健康检测的方法。     

浅谈影响超声回弹综合法检测精度的主要因素

列举了影响超声回弹综合法检测精度的主要因素,并结合部分试验数据进行分析说明,为现场检测工作提供指导。     

基于Web的TADS实时监控功能设计

介绍车辆滚动抽承故障轨边声学检测承统(TADS)实时监控的重要意义,采用ASP.NET技术,实现了基于Web的TADS实时监控页面设计,将列车探测监控、轴承报警监控、设备状态监控等多项功能集成为一个综合监控平台,并应用AJAX技术实现实时监控页面的无闪烁更新,解决监控数据按不同用户过滤显示的问题,可以为用户提供更好的使用体验.     

新华立交桥加固前后承载能力对比分析

通过对加固前后的新华立交桥的检测结果进行比较,分析该桥在加固以后的承载能力及刚度变化情况,检验该桥的加固效果对桥梁耐久性及承载力安全储备的提高。从而看出定期检查对于桥梁承载能力的重要性,从检测结果分析病害原因,为加固设计提供依据。     

液压式检漏方法和装置的研究

在实际的工作中经常能遇到机械设备液压系统和管路系统发生漏水、漏油、漏气的问题。这种液压系统泄漏不仅能产生严重的安全问题,而且还会影响设备的技术性能和经济性能,甚至还会损坏设备、停工,停产,乃至产生环境污染。本文分析了液压系统泄漏的原因和对常用检漏方法进行比较并介绍了一种新型液压式自动检漏装置。     

Real-Time Transportation Mode Detection Using Smartphones and Artificial Neural Networks: Performance Comparisons Between Smartphones and Conventional Global Positioning System Sensors

Traditionally, traffic monitoring requires data from traffic cameras, loop detectors, or probe vehicles that are usually operated by dedicated employees. In efforts to reduce the capital and operational costs associated with traffic monitoring, departments of transportation have explored the feasibility of using global positioning system (GPS) data loggers on their probe vehicles that are postprocessed for analyzing the traffic patterns on desired routes. Furthermore, most cell phones are equipped with embedded assisted-GPS (AGPS) chips, and if the mode of transportation the phone is in can be anonymously identified, the phones can be treated as if they are probe vehicles that are voluntarily hovering throughout the city, at minimal additional costs. Emerging cell phones known as “smartphones” are equipped with additional sensors including an accelerometer and magnetometer. The accelerometer can directly measure the acceleration values, as opposed to having acceleration values derived from speed values in conventional GPS sensors. The magnetometer can measure mode-specific electromagnetic levels. Smartphones are subscribed with roadside Internet data plans that can provide an essential platform for real-time traffic monitoring. In this article, neural network-based artificial intelligence is used to identify the mode of transportation by detecting the patterns of distinct physical profile of each mode that consists of speed, acceleration, number of satellites in view, and electromagnetic levels. Results show that newly available values in smartphones improve the mode detection rates when compared with using conventional GPS data loggers. When smartphones are in known orientations, they can provide three-dimensional (3-D) acceleration values that can further improve mode detection accuracies.