用容错控制技术提高微机保护系统的可靠性

在介绍容错控制技术基本理论的基础上,将容错控制技术应用到微机保护系统中,提出微机保护系统中应用容错控制技术的基本准则及常用方法.在分析一些常见故障的容错控制方案的基础上,重点分析了针对微机保护系统传感器故障的容错控制,分析了其故障检测及容错的工作原理.以牵引供电系统的馈线保护为例,介绍了容错控制技术在该保护系统中的应用方法.采用容错控制技术为提高微机保护装置的可靠性提供了一条新的途径.     

Power transformer fault diagnosis model based on rough set theory with fuzzy representation

Objective Due to the incompleteness and complexity of fault diagnosis for power transformers, a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented. Fuzzy set theory is used both for representation of incipient faults＇ indications and producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space. The fuzzy membership functions corresponding to the indicative regions, modelled by rules, are stored as cases. Results Diagnostic conclusions are made using a similarity measure based on these membership functions. Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis. Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.     

小波包-神经网络在摆式列车倾摆控制系统故障诊断中的应用

针对摆式列车倾摆控制系统故障的特点，研究了神经网络结合小波包分析进行故障诊断的方法，采用小波包分解和信号重构的方法，将在摆式列车试验台上采集到的振动信号分解到不同的频带以提取有关的故障信息，并将振动信号各频带内的能量特征作为训练样本输入前向神经网络，用优于改进梯度下降法的Levenberg—Marquardt优化方法对网络进行训练，对倾摆控制系统的常见故障进行识别和诊断。实践表明，该方法对摆式列车倾摆系统故障的诊断是可靠的。     

锂离子电池内短路形成机理及检测方法综述

对锂离子电池内短路的形成机理及其检测方法进行研究。从内部缺陷、机械滥用、电滥用和热滥用等4个方面对内短路的形成机理进行分析。针对基于模型驱动、传感器信号和机器学习的内短路检测方法进行研究,深入分析不同技术的特点。通过机理分析,得到减少内短路的措施。针对当前检测技术所面临的难点和问题,结合大数据、5G通信和车端云智能网联技术,提出未来锂离子电池内短路检测方法的发展方向。     

EPS系统故障模式的分析

针对EPS控制系统故障诊断与系统保护策略的开发，在分析EPS控制系统工作原理的基础上，运用FTA以及FMEA方法，对故障模式进行了详尽的分析，并在此基础上对这些故障模式进行了归类，为下一步的故障检测奠定了基础。     

机电一体化设备的故障诊断技术研究

专家学者进行研究,依托于现代有利的条件最终研发出了一种信息处理技术—机械一体化设备的故障诊断技术。该项技术具有非常突出的特点,在机械设备广泛应用于各行业的现代社会能够经常看到该技术的身影,各大行业加大该技术的应用力度,这在向我们传递一个讯息:机械一体化设备的故障诊断技术作用越来越突出。我们可以看到当前的生产工具不断改进,生产力同以往相比有所提高。在科技的助力下最终实现了机电一体化。设备具备了以往没有的控制与信息处理的各项功能。而机械装置功能的越发强大会给故障诊断工作带来一定的压力。在这样的背景下机电一体化设备的故障诊断技术出现。技术的出现也算是迎合发展的一种表现。     

浅谈比亚迪E5电池管理系统故障诊断与排除

本文根据2018款比亚迪E5结构与原理,通过对比亚迪E5车辆电池管理系统的故障进行分析、诊断,并结合维修手册和相关资料去思考,找到该故障所在位置,排除车辆无法上电的故障,恢复该车正常行驶。这充分证明了如何运用所掌握的知识和原理,以及车辆新技术,并结合维修手册和相关资料去思考和解决车辆故障修理难题。     

基于AVL BOOST的船用柴油机典型故障仿真及其数据分析

[目的]大型船用柴油机故障类型的数据通过台架试验或者实船来获取存在许多不利因素,因此针对柴油机的故障仿真数值计算就显得尤为重要,同时对故障排除及数据驱动的智能故障诊断系统的构建也具有重要意义。[方法]基于AVL BOOST软件和台架试验数据,建立柴油机仿真模型,验证4种负荷工况下仿真模型需满足的精度要求;基于100%负荷工况模型,采用控制变量法模拟柴油机发火点提前、单缸停油及曲轴箱窜气这些典型故障,并分析计算得到的数据。[结果]结果表明:发火点提前5°时,缸内最高燃烧压力提高了17.4%;第1缸停缸后,有效油耗率上升近15%;对于不同气缸停油情况,第2号和3号气缸停油时的特征参数变化幅度较小;随着活塞有效窜气间隙的增加,各特征参数基本上呈线性扩大趋势,在窜气间隙值为0.04 mm时,部分特征参数急剧增加,例如油耗率增加了近40%。[结论]所得结果可作为柴油机故障状态识别及智能故障诊断系统构建的重要依据,为探索船舶柴油机智能故障诊断技术提供新的途径。     

Application of an improved EEMD method in bearing fault diagnosis of induction motors北大核心CSCD

[Objective]In order to overcome the disadvantages of the traditional ensemble empirical mode decomposition (EEMD) method in selecting parameters (integration time and white noise amplitude coefficient) based on experience, and reduce the cost of calculation time, a fast ensemble empirical mode decomposition (FEEMD) method is used to extract the characteristic frequency. [Method]By changing the distribution density of the added white noise, different signal envelopes can be obtained. Furthermore, we can identify the optimal envelope by finding the optimal search window width of the moving mean filter, thereby avoiding the defect of EEMD selecting parameters by experience. At the same time, after the abnormal component in the signal is decomposed, the residual component can be decomposed by EMD to further save the calculation time cost. Finally, the method is combined with Hilbert envelope demodulation technology and applied to the fault characteristic frequency diagnosis of the bearing inner ring of an asynchronous motor. [Results]As the results show, compared with the traditional EEMD method, FEEMD can extract the fault frequency more efficiently. [Conclusion]FEEMD overcomes the disadvantages of the traditional EEMD method in selecting parameters based on experience and shortens the calculation time. As such, it can be effectively applied in bearing fault frequency extraction experiments. © The Author(s) 2022.     

Fault diagnosis of steam power system based on convolutional neural network北大核心CSCD

[Objectives]In order to improve the fault diagnosis level of marine power systems, this paper studies the real-time fault diagnosis of a marine supercharged boiler based on a convolutional neural network (CNN).[Methods]First, the simulation program of the marine supercharged boiler is developed based on the GSE platform, and the simulation fault data is obtained. The fault diagnosis model of the boiler is then established using the CNN method. Next, through the change trends of temperature, flow and other parameters, combined with a priori knowledge and the machine learning method, fault identification is carried out. Lastly, the performance of the method is evaluated against criteria such as confusion matrix and accuracy. [Results]According to the comparison results between the feature extracted dataset and the original dataset, the stability of the model output results and the generalization ability of the model are optimized and improved, with an overall fault classification accuracy reaching 99.53%.[Conclusion]The results of this study can provide valuable references for the intelligent monitoring of marine power systems. © 2022 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.