基于FR-CNN算法的线路巡检元件故障识别仿真

由于传统故障识别方法的特征提取精度较低,得出的线路巡检元件故障识别的误差较大.于是提出基于FasterR-CNN算法的线路巡检元件故障识别方法.根据线路巡检元件的运行规律划分其状态,并设置相应的状态特征,作为元件故障识别的比对标准.利用巡检机器人内置的硬件设备,实时收集线路巡检图像,并通过压缩和透视畸变校正两个步骤,实现对巡检图像的预处理.构建FasterR-CNN网络模型,并利用该模型提取巡检元件图像特征,并针对防震锤、间隔棒和绝缘子等元件得出最终的故障识别结果.经过仿真测试得出结论,与传统识别方法相比,设计识别方法的mAP指标值提高了0.316,即识别精度有所提升,且识别耗时较短,具有一定的有效性.     

基于无线通信网络技术的机器人巡检远程监控系统

该文提出一种基于无线通信网络技术的机器人巡检远程监控系统,监控输电线路故障。系统的硬件设计包括主控模块设计、无线通信网络设计、驱动器接口电路设计、远程监控模块设计;系统软件设计主要是在远程监控模块利用增量式PID控制算法控制机器人巡检驱动,实现机器人巡检远程监控。实验结果表明,该系统可控制巡检机器人巡检输电线路、监测输电线路故障状态,可监测不同风速下线路风偏角变化情况,且该系统具备较好的信息传输能力和运行可靠性。     

矿用带式输送机巡检机器人驱动系统设计

目前矿用带式输送机巡检机器人的研究主要针对带式输送机巡检机器人故障识别与诊断等方面,忽视了巡检机器人运动问题。煤矿井下巷道设备较多,作业空间狭小且地形复杂,巡检机器人运动时会遇到爬坡、煤泥障碍等极端路面情况。鉴于矿用带式输送机巡检距离较长、巡检目标相对单一且巡检路线固定,采用轨道式传动作为巡检机器人的行走方式。但该方式在轨道面附着煤泥的情况下驱动轮会卡死,且面对坡度较大的轨道时可能发生打滑现象,因此设计了一种四轮支撑、两轮驱动的轨道式驱动系统,巡检机器人依靠驱动轮与轨道之间的摩擦向前运动,支撑轮承载巡检机器人的质量并起到辅助行走的作用。对巡检机器人驱动系统主要零件传动轴和摆臂进行了有限元仿真分析,得到传动轴和摆臂的极限应力分别为83.2,65.8MPa,远低于材料的屈服强度,保证了巡检机器人的可靠性。对巡检机器人驱动系统的爬坡和煤泥越障性能进行了试验,结果表明,巡检机器人在25°斜坡轨道上仍可完成加速运动,且在上下坡过程中运行平稳,在煤泥障碍轨道上运行没有发生打滑和卡死现象。     

基于RPA的审计抽样软件机器人故障自动识别系统研究

在审计抽样软件机器人运行状态下,受到样本数据之间差量分布影响,机器人抽取策略无法精准映射到每一个样本个体上,导致抽取的样本存在差量偏移,失去了抽取样本的客观性。为了避免这一问题,通常采用故障识别系统对其异常数据进行识别,但是与传统数据故障不同,神经抽样数据的差量精度较高,对应特征系数精度远超传统识别系统的识别阈值,因此,传统的识别系统的识别结果整体输出误差偏大,且识别效果稳定相差。基于审计抽样数据的特征,引入机器人流程自动化(Robotic Process Automation, RPA),根据审计流程数据特点,构建硬件平台;在硬件平台基础上,设计RPA软件流程,通过对RPA审计数据故障点向量机模型的建立、抽样数据故障特征识别与RPA故障识别数据判定输出,完成提出系统的设计,实验数据表明：经过RPA算法优化后的系统,整体故障识别率明显提升,且系统稳定性远远优于现有识别系统的参数标准,能够满足审计抽样软件机器人故障自动识别的准确度要求。     

燃煤电站输煤廊道挂轨巡检智能机器人障碍物识别定位测距系统研究

受到燃煤电站输煤廊道环境空间与环境条件的影响,现有廊道挂轨巡检机器人测量系统对障碍物识别定位测距误差较大,导致整体巡检任务执行时间过长,巡检效率偏低。究其原因在于系统对障碍物感知、认知算法上存在参量缺陷,结合燃煤电站输煤廊道特点,对燃煤电站输煤廊道挂轨巡检智能机器人障碍物识别定位测距系统展开研究。研究从系统障碍物识别算法入手,通过引入视觉算法、自主识别算法与多元信息融合算法,分别对机器人的障碍物识别特征、廊道环境特征以及障碍物空间分布特征加以优化,实现机器人障碍物识别定位测距精准度的提升,改善识别响应速度的效果。通过模拟环境下与传统挂轨巡检智能机器人障碍物识别定位测距系统R2的数据对比,证明提出研究方向的正确性与有效性,且优化后的挂轨巡检智能机器人障碍物识别定位测距系统应用效果更好。     

基于LoRa的变电站巡检监控系统的设计

针对变电站巡检技术的发展需求,设计了一种基于巡检机器人的巡检监控系统,该系统结合LoRa物联网技术和计算机嵌入式技术,与电网设施环境结合,能够进行无线视频监测,对机器人位姿、环境温度、前向障碍进行实时采集,将采集图像和数据发送至终端,并能够通过终端操作,对机器人进行运动控制,使其巡检变电站的各个监测环节。测试结果表明,该系统能够实现机器人的远程监测和运动控制,能够有效减少变电站人员工作量,改良变电站巡检方式。     

基于物联网技术的电力变压器动态运检系统设计

在加强智能电网的建设中，电力变压器是重要的一环，其运检工作是确保整个变电站能够正常运行的根本。为实现更加智能、科学的动态运检，设计基于物联网技术的电力变压器动态运检系统。基于物联网技术设计射频识别模块，由射频标签、编程器等构成，实现电力变压器设备的自动化识别与系统的双向非接触数据通信功能。在线监测模块主要通过变压器智能组件柜实现电力变压器的在线监测。在巡检模块的设计中，为实现动态巡检，设计悬挂式巡检机器人，为其配置吊挂轨道，轨道经过站内所有电力变压器设备，使轨道尽量远离人员和柜体设备，实现电力变压器的巡检。在检修模块中，结合可拓学与粗糙集理论设计故障诊断算法，实施电力变压器的故障诊断。系统测试结果表明，对于主变故障与站用变故障，系统的故障诊断误差率均较低；在5台主变的巡检中，系统仅使用了90分钟左右的时间；在8台站用变的巡检中，系统仅使用了113分钟左右的时间；系统的故障定位准确率较高。     

基于TurtleBot3和ROS的楼宇智能巡检机器人

为了在公共楼宇或建筑中,利用可移动巡检机器人替代或补充传统的人工巡检,实现更加有效可靠的自动化安全管理目标.采用TurtleBot3轮式移动机器人平台,搭载激光雷达、六麦克风语音阵列、可见光摄像头、红外热像仪和环境传感器等模块,以Jetson TX2为核心控制板,以ROS(Robot Operating System,机器人操作系统)为软件平台,开发专用于楼宇安全巡检的智能机器人.该机器人具有SLAM(Simultaneous Localization and Mapping,同时定位与地图构建)和路径规划、语音识别、设备故障检测、环境监测和报警功能.同时,作为移动传感终端,该机器人可与智慧楼宇物联网主系统对接,具有很强的灵活性和扩展性.     

带式输送机故障巡检机器人系统设计

针对现有带式输送机故障检测方法劳动强度大、可靠性较差等问题,提出了一种带式输送机故障巡检机器人系统设计方案。首先分析了带式输送机常见的输送带和托辊故障类型及其原因,包括输送带横向断裂、纵向撕裂、打滑、跑偏及堆煤,托辊磨损和转动卡死等;然后针对不同故障类型,选定了系统传感器,包括激光雷达、单目相机、烟雾传感器、声音传感器和温度传感器,并提出了相应的故障检测算法;最后给出了系统上位机和下位机软件流程。     

基于电站巡检机器人监控系统的设计

针对电站传统的监测方法所需要的人员多、工作量大以及缺乏实时分析故障等缺点,设计了一款智能的电站巡检机器人的监测系统。所设计的机器人具有自主循迹,智能判断故障,记录事故原因及时报警等特点。详细的阐述了硬件的设计方法以及软件的设计方案。并对电站的异常情况进行了模拟测试,得出所设计的系统满足性能指标,具有一定的工程应用价值。     

基于神经网络技术的机器人并发故障自动诊断方法

为了提高柔性负载抓握机器人的故障检测能力,提出基于神经网络技术的机器人并发故障自动诊断方法.运用高分辨的智能传感器信息识别技术,结合刚度和强度等机械结构特征分析,构建柔性负载抓握机器人的故障信息采集模型,采用变刚度原理,提取柔性负载抓握机器人的振荡信息特征,通过谱特征检测和动态信息融合进行柔性负载抓握机器人的故障信息的...     

Variable reconstruction and sensor fault identification using canonical variate analysis

The detection and identification of faults in dynamic continuous processes has received considerable recent attention from researchers in academia and industry. In this paper, a canonical variate analysis (CVA)-based sensor fault detection and identification method via variable reconstruction is described. Several previous studies have shown that CVA-based monitoring techniques can effectively detect faults in dynamic processes. Here we define two monitoring indices in the state and noise spaces for fault detection and, for sensor fault identification, we propose three variable reconstruction algorithms based on the proposed monitoring indices. The variable reconstruction algorithms are based on the concepts of conditional mean replacement and object function minimization. The proposed approach is applied to a simulated continuous stirred tank reactor and the results are compared to those obtained using the traditional dynamic monitoring technique, dynamic principal component analysis (PCA). The results indicate that the proposed methodology is quite effective for monitoring dynamic processes in terms of sensor fault detection and identification.     

Hydraulic turbine governing system identification using T–S fuzzy model optimized by chaotic gravitational search algorithm

Hydraulic turbine governing system (HTGS) is a complicated nonlinear system that controls the frequency and power output of hydroelectric generating unit (HGU). The modeling of HTGS is an important and difficult task, because some components, like hydraulic turbine and governor actuator, are with strong nonlinearity. In this paper, a novel Takagi–Sugeno (T–S) fuzzy model identification method based on chaotic gravitational search algorithm (CGSA) is proposed and applied in the modeling of HTGS. In the proposed method, fuzzy c-regression model clustering algorithm is used to partition the input space and identify the coarse antecedent membership function (MF) parameters at first. And then, a novel CGSA is proposed to search better MF parameters around the coarse results, in which chaotic search has been embedded in the iteration of basic GSA to search and replace the current best solution of GSA. The performance of the proposed fuzzy model identification method is validated by benchmark problems, and the results show that the accuracies of identified models have been improved significantly compared with the other existing models. Finally, the proposed approach has been applied to approximate the dynamic behaviors of HTGS of a HGU in a hydropower station of Jiangxi Province of China. The experimental results show that our approach can identify the HTGS satisfactorily with acceptable accuracy.     

Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that existing work suffers from inherent limitations if complex fault scenarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.     

Finding fault - fault diagnosis on the wheels of a mobile robot using local model neural networks

As mobile robots are mostly designed to act autonomously, procedures that detect and isolate faults on the various parts of a robot are essential. The most powerful approaches in fault detection and isolation (FDI) are those using a process model, where quantitative and qualitative knowledge-based models, databased models, or combinations thereof are applied. This article suggests a model-free approach to the solution of the fault detection problem. One way to deal with the absence of a mathematical model is to build a model from input-output data. In this article, local model networks (LMNs) are used for plant modeling. The key to fault detection and diagnosis is the creation of residual signals. Although the way these signals are formed varies, in all cases the residuals change their value accordingly with the presence of faults. To avoid false alarms, the residuals must be affected by factors unrelated to faults (like modeling errors) as little as possible. Change-detection algorithms are therefore used for reliable residual generation. These algorithms are designed to detect changes in signals that include noise or other types of disorders. The combination of local model networks for modeling and change-detection algorithms for residual creation provides an efficient method for fault detection and diagnosis. The method is applied on the wheels subsystem of a mobile robot.     

基于广义回归神经网络的传感器故障诊断研究

针对诊断传感器偏置故障与漂移故障的难点问题,提出了一种基于广义回归神经网络(GRNN)的传感器故障诊断方法。该方法充分利用控制系统闭环回路测控信息,建立一组多输入单输出GRNN观测器,通过将观测器输出与传感器实际输出相比较获取残差序列,获得基于残差序列的传感器偏置故障和漂移故障的辨识策略,实现控制系统传感器故障在线诊断。仿真结果表明:该方法可以快速准确地检测和分离传感器故障,辨识传感器故障类型、故障大小以及故障发生的时间。     

基于遗传算法的机械设备故障检测机器人设计

当前故障检测机器人受到超声波影响故障检测存在精准度低的问题,据此提出了基于遗传算法的机械设备故障检测机器人设计。采用AD500-1A型号传感器采集机械设备内外部数据信息,使用等效转换电路使机器人实时感知周围环境变化信息,并利用灵敏度高电子仪器实现机器人传感工作;使用2路200万数字网络高清摄像头,监视整个机械设备,获取机器人结构通信、管理和运动信息;将proGee0813型号芯片作为导航设备定位芯片,根据实际需求获取信号指令,并选定机器人行驶路径;通过Unity与UE4引擎虚拟现实硬件交互设备进行故障定位追踪;利用关节装置连接车轮前臂和上臂,实现不同磁铁吸附与脱离,依据机器人结构,完成机器人硬件结构设计。采用遗传算法确定导航适应度函数,通过机器人视频采集信息,设计预警功能,并利用机器人即时生成设备故障图像,依据实现流程,在超声避障功能支持下,完成机械设备故障检测。由实验结果可知,该机器人检测精准度最高可达到0.96,提高了机器人检测鲁棒性。     

Digital twin-assisted fault diagnosis system for robot joints with insufficient data

The robot joint is an important component of the construction robot, and its fault diagnosis can ensure the exact execution of building jobs, stable operation, and timely prevention of probable safety mishaps. However, deep learning-based fault diagnosis needs a multitude of measured fault data, which is difficult to obtain for various reasons. To solve the problem of insufficient data, a digital twin-assisted fault diagnosis system for robot joints is proposed. First, a simplified dynamics model of the robot joint is developed to generate the virtual entity data which can be used as the X-domain data for the digital twin model. Second, a CycleGAN-based digital twin model is proposed to map the virtual entity (X-domain) data to the physical entity (Y-domain) utilizing only a small amount of measured data. In the end, a test-rig for the robot joint is built to simulate the robot's working conditions, and the CNN-ResNet classifier is utilized to verify the effectiveness of the simulated data generated by the digital twin model. The results show that the fault diagnosis accuracy can be increased from 32.5% to 98.86% utilizing only 400 sets of measured data.     

配电网故障快速定位系统研究

提出了一种配电网故障快速定位系统的设计方案。该系统采用柱上故障监测终端识别故障,采用控制主站定位故障区段,两者通过GPRS通信管理机交换数据;采用过流速断法识别短路故障,采用全电流法识别接地故障;针对故障信息缺失及畸变情况,采用三态标识法对传统故障区段定位算法进行改进。仿真结果表明,在故障信息缺失和畸变的情况下,改进的故障区段定位算法可准确定位故障,容错性高。     

基于递归稀疏主成分分析的工业过程在线故障监测和诊断

提出一种基于递归稀疏主成分分析(recursive sparse principal component analysis,RSPCA)的工业过程故障监测与诊断方法,可用于时变工业过程的自适应故障监测与诊断.通过引入弹性回归网,将主成分问题转化为Lasso与Ridge结合的凸优化问题,采用秩-1矩阵修正对协方差矩阵进行递归分解,递归更新稀疏载荷矩阵和监测统计量的过程控制限,以实现连续工业过程长时间自适应故障监测,对检测出来的故障通过贡献图法实现对故障的诊断.在田纳西-伊斯曼(TE)过程进行实验验证,结果表明,与传统的故障监测方法相比,所提出的方法有效降低了故障漏检率和误报率,且时间复杂度低,确保了故障监测的灵敏度和实时性.