A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

影响音视频电子产品安全测试的主要试验条件的探讨

通过对影响音视频产品安全测试的主要条件分析,帮助检测人员更好地理解这些主要测试条件,并在音视频产品的安全检测中,能对这些条件进行最不利的组合,确保产品达到标准的安全要求.     

基于云计算的云找回信息资讯平台的开发与实现

为解决信息时代下的物品丢失问题,特设计出一款基于云计算的云找回信息资讯平台.该平台利用Web技术,巧妙结合Springboot框架、云计算思想下对数据迁移和云数据库的部署将传统的告示形式的失物招领系统转变为网络可视化智能匹配方式,将失主与拾主联系在一起,架起用户之间的桥梁.本文着重阐述系统的总体结构和框架,说明了详细的...     

基于特征结构配置的飞控系统故障检测与诊断

针对具有未知扰动输入的飞行控制系统,运用特征值配置设计了一种用于故障检测和诊断的观测器,他通过对观测器进行左特征向量的配置使得残差与干扰分布方向正交,故障检验残差r与未知输入干扰d之间的传递函数阵为零,从而使残差与干扰直接解耦.通过这种方法,残差信号得以对干扰具有鲁棒性,使FDI算法不受系统不确定性干扰的影响,提高系统故障诊断的可靠性和精度.同时通过残差信号估计故障,能在线辨识故障的形态,仿真结果验证了该方法的有效性.     

一种具有自动纠错功能的FIR滤波器研究

提出了一种有效实现自动纠错功能FIR数字滤波器技术,该技术采用2种不同架构的标准滤波器通过并行操作来完成。任一滤波器软错误的发生就会引起两个滤波器输出不匹配,达到检测错误的目的,增强了传统滤波器对差错检测和差错纠正的支持。最后对该滤波器地性能进行评估,该滤波器性能良好,纠错率接近100%,可广泛运用在各种信号处理中。     

存储器故障诊断算法的研究与实现

集成电路工艺的改进使存储器的测试面临着更大的挑战。文中从存储器的故障模型入手,着重描述了存储器常见的诊断算法。诊断算法和诊断策略要在诊断时间、故障覆盖率、面积开支之间进行权衡。因此要根据存储器的故障类型和测试需求来选择合适的诊断算法,才能达到比较满意的效果。     

基于小波理论的变压器励磁涌流的研究

变压器差动保护受励磁涌流的影响,针对这一因素,该文提出一种基于小波变换的变压器励磁涌流识别新方法。首先分析了单相变压器励磁涌流的产生机理,其次使用MATLAB提供的电力系统工具箱PSB建立励磁涌流与区内故障电流的仿真模型,并脂db5小波对电流信号进行3尺度分解,仿真结果表明,小波变换系数能准确反应励磁涌流波彤的突变,最后根据仿真结果提出一个励磁涌流识别判据。     

模拟电路故障诊断的信息融合新方法

针对测试信息不足造成模拟电路故障诊断准确率较低的问题,为充分利用有限测试信息,提出一种模拟电路故障诊断信息融合新方法.首先,将采集的故障样本集变换到不同特征空间,然后利用所提出的马氏距离分布熵求取各特征空间的相对优势分类集,在此基础上,定义相对优势属性约简提取各特征空间的局部最佳可分性信息,最后,对基分类器结合所提出的自适应类模糊密度赋值方法进行模糊积分融合.国际标准电路故障诊断实例表明,所提方法能有效提高模拟电路的故障诊断率.     

基于BP神经网络的某型飞机火控系统故障诊断

根据某型飞机火控系统的特点,给出了BP神经网络数学模型及其学习算法,在此基础上以某型飞机火控系统作为为被诊断对象,运用BP神经网络数学模型及其学习算法对其进行故障诊断。诊断结果表明BP神经网络不仅能识别出样本自身的故障,而且能准确诊断出样本以外数据故障,提高某型飞机火控系统一线维修保障效率。