Application of Machine Learning in Electronic Device Fault Diagnosis

As electronic devices become increasingly complex, traditional fault diagnosis methods face significant challenges. Machine learning technologies offer new opportunities and solutions for electronic device fault diagnosis. This paper explores the application of machine learning in electronic device fault diagnosis, focusing on common machine learning algorithms, data preprocessing techniques, and diagnostic model construction methods. Case study analysis elucidates the advantages of machine learning in improving diagnostic accuracy, reducing diagnosis time, and implementing predictive maintenance. Research indicates that machine learning techniques can effectively enhance the efficiency and precision of electronic device fault diagnosis, providing robust support for device reliability and maintenance strategy optimization. In the future, as artificial intelligence technology further develops, machine learning will play an increasingly important role in the field of electronic device fault diagnosis.

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion,the increasing attention on power electronic equipment is fault detection and diagnosis.A power electronic circuit is an essential part of a power electronic system.The state of its internal components affects the performance of the system.The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits.Therefore,an algorithm based on adaptive simulated annealing particle swarm optimization(ASAPSO)was used in the present study to optimize a backpropagation(BP)neural network employed for the online fault diagnosis of a power electronic circuit.We built a circuit simulation model in MATLAB to obtain its DC output voltage.Using Fourier analysis,we extracted fault features.These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization(PSO)and the ASAPSO algorithm.The accuracy of fault diagnosis was compared for the three networks.The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy,better reliability,and adaptability and can more effectively diagnose and locate faults in power electronic circuits.

A Hierarchical Modeling and Fault Diagnosis Method for Complex Electronic Devices

Due to the shortcomings of the diagnosis systems for complex electronic devices such as failure models hard to build and low fault isolation resolution, a new hierarchical modeling and diagnosis method is proposed based on multisignal model and support vector machine （SVM）. Multisignal model is used to describe the failure propagation relationship in electronic device system, and the most probable failure printed circuit boards （PCBs） can be found by Bayes inference. The exact failure modes in the PCBs can be identified by SVM. The results show the proposed modeling and diagnosis method is effective and suitable for diagnosis for complex electronic devices.

Application of Improved Deep Auto-Encoder Network in Rolling Bearing Fault Diagnosis

Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.

Research on Genetic Algorithm Based Knowledge Auto Acquisition for Fault Diagnosis

In this paper, a genetic algorithm based knowledge auto acquisition approach for fault diagnosis is proposed. Under the circumstances that diagnostic examples are available but no empirical knowledge can be obtained, knowledge for fault diagnosis can be

Fault Diagnosis for Rolling Bearings with Stacked Denoising Auto-encoder of Information Aggregation

Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.

A Fuzzy Mathematics Based Fault Auto-diagnosis System for Vacuum Resin Shot Dosing Equipment

On the basis of the analysis of faults and their causes of vacuum resin shot dosing equipment, the fuzzy model of fault diagnosis for the equipment is constructed, and the fuzzy relationship matrix, the symptom fuzzy vector, the fuzzy compound arithmetic operator, and the diagnosis principle of the model are determined. Then the fault auto-diagnosis system for the equipment is designed , and the functions for real-time monitoring its operation condition and for fault auto diagosis are realized. Finally, the experiments of fault auto-diagnosis are conducted in practical production and the veracity of the system is verified.

Fault diagnosis for distillation process based on CNN–DAE

Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.

基于NSSAE的批次发酵过程质量相关与质量无关故障检测与诊断

为了解决批次发酵过程中质量无关故障所可能引起的不必要停机,本文提出了噪声半监督堆叠自编码器(Noised semi-supervised stacked auto-encoder,NSSAE)算法以区分质量相关与质量无关故障。首先,基于互信息计算过程变量与质量变量间互信息,并对数据加入噪声以提高算法对质量相关信息挖掘能力。其次,构建NSSAE的过程监测模型,在模型的首层自编码器和最后一层自编码器中构建故障检测和质量相关检测指标,并利用核密度估计计算对应的控制极限。最后,利用深度重构贡献图(Deep reconstruction-based contribution,DRBC)定位故障根源。从数值仿真和乳酸菌批次发酵实验结果可知,本文提出的NSSAE算法能够准确区分质量相关与无关故障,首层的残差空间的检测指标的故障检测率接近100%,最后一层隐空间的检测指标能够准确识别质量相关故障和质量无关故障。基于DRBC诊断方法能在故障发生后准确识别发生故障的变量,该研究结果为批次发酵过程质量相关与质量无关故障监测问题提出了一种切实可行的过程监测方法。

基于虚拟化技术的电子信息设备故障诊断与虚拟仿真实验室设计

阐述使用虚拟化技术构建电子信息设备故障诊断虚拟仿真实验室的方案,通过使用虚拟化技术将电子信息设备的硬件环境虚拟化,使用仿真软件模拟设备的运行状态和故障场景,实现设备的故障诊断。

跳连接变分自编码器与CNN相结合的滚动轴承故障诊断方法

针对滚动轴承故障率小、不易收集故障数据的问题,提出基于跳跃连接变分自编码器与宽核深度卷积神经网络相结合的小样本故障诊断方法。该方法首先在变分自编码器的编码和解码之间引入跳跃连接结构,并将Tanh作为网络的激活函数,进而提高生成样本的特征多样性;其次,构建宽核深度卷积网络诊断模型,该模型可以提高从振动信号中提取故障特征的能力;最后,经生成样本扩充的数据集作为模型输入,提高训练集包含的特征信息量,实现小样本下的故障诊断。实验分析表明,所提方法在小样本情形下能生成有效的伪样本并具有较高的诊断精度。

基于堆叠稀疏判别自编码的滚动轴承智能故障诊断方法

针对复杂工况下旋转部件故障诊断精度不理想、稳定性差的问题,提出一种堆叠稀疏判别自编码智能故障诊断方法。首先,构造堆叠稀疏自编码网络结构,利用KL散度限制多层网络的稀疏性以增强深度自编码网络的学习性能;其次,采用半监督机制对上述网络进行改进,利用标签信息对分类层进行调整,增强自编码网络的分类能力;然后,提出样本结构特征约束,通过增加类几何特征惩罚项和类数据特征惩罚项对网络隐藏层进行约束,自适应判别聚合距离和分类距离,提升整体网络的特征提取能力;最后,在美国凯斯西储大学轴承数据集上验证所提方法的可靠性。试验结果表明,与现有方法相比,堆叠稀疏判别自编码智能故障诊断方法在多组数据集上均能够获得更高的诊断精度,具有良好的稳定性。

基于图正则化堆叠自编码器的风机轴承故障诊断方法

【目的】为解决风电机组轴承故障诊断中的故障特征提取效率低、特征表示不够精准以及现有方法难以适应复杂信号需求的问题,提出一种基于图正则化的故障诊断方法,提升了对振动信号的分析能力,从而实现对不同故障类型的准确分类和可靠诊断。【方法】采用基于图正则化自编码器的技术,结合图嵌入思想,指导堆叠监督自编码器进行特征提取。在故障特征提取阶段,首先对诊断信号进行图表示,然后在堆叠自编码器中添加图正则化项,以确保嵌入后的低维特征保持流形结构,从而提取数据深层的复杂几何特征。【结果】所提取的特征能够实现对不同故障类型的准确分类,展现出在故障特征捕捉方面的显著优势。实验结果表明,该方法在实际风场数据的应用中,具有更高的诊断精度和可靠性,有效提高了故障特征的提取效率和分类准确性。【结论】所提方法在风电轴承故障诊断领域表现出显著的有效性和优越性,为实际应用提供了可靠的技术支持。

基于BIM的智能运维管理监控系统设计与实现

为切实提升生产设备的信息化和智能化水平、提高生产效率和故障诊断能力,设计一套基于BIM的智能运维管理监控系统。系统将多源传感器数据融合,结合BIM技术进行展示,并引入基于专家经验转化的故障诊断技术。该系统已保障某场站安全运行生产200余天,达到很好的预期。同时,系统产生160 GB原始监测数据和专家经验标定结果,为后续引入基于机器学习的故障诊断方法提供原始训练数据支持。

飞机电源系统故障诊断方法综述及发展趋势

飞机电源系统是机上一切用电设备的电能来源,其安全性与可靠性至关重要。在环保和高效发展需求的背景下,现代航空工业正在推进以电能为核心的多电/全电飞机技术的研究和应用。电驱动装置和电力电子器件的广泛使用导致飞机电源系统结构的复杂化,对飞机的可靠性、安全性、测试性和维修性提出了更高的要求,研究飞机电源系统的故障诊断技术具有重要意义。本文首先介绍了飞机电源系统的组成结构和各自功能,概述了飞机电源系统的发展历程,对比分析国内外典型电源系统的特征,总结了飞机电源系统中的主要故障模式、故障特点和失效原因,并提出一种飞机电源健康管理系统的设计架构,然后综述了国内外基于模型和基于数据的故障诊断方法研究进展,从准确度、数据需求量、适用性和实现难易程度等方面评述了各类诊断方法的特点,最后指出了飞机电源系统故障诊断技术面临的挑战和发展趋势。

基于自编码多层感知器的电力负荷管理终端运行故障诊断方法

针对现有电力负荷管理终端运行故障辨识率低问题,提出一种基于自编码多层感知器的电力负荷管理终端运行故障诊断方法。首先,分析负荷管理终端运行状态指标与故障;其次,阐述自编码多层感知器电力负荷管理终端运行的故障诊断模型及其实现方法,通过终端运行技术指标时序数据的等间隔式滑动窗口,操作获取表征终端运行状态特征候选集合,采用自编码器AE特征选择子模型,提取最优特征子集,借助多层感知器MLP故障诊断辨识子模型,实现终端运行故障分类诊断;最后,通过实验验证所提方法有效性。结果表明所提方法能提升电力负荷管理终端运行故障辨识精确率、召回率和辨识效率,实现电力负荷管理终端运行故障的精细化诊断。

JSM 6360型扫描电镜故障诊断与维护控制

利用JSM 6360型扫描电子显微镜配合能谱仪能较好地对材料进行微观结构与微区元素分析。介绍了JSM 6360型扫描电镜的基本控制要点、日常故障分析与处理措施及维护创新点,分析了对钨灯丝过早熔断、电镜x轴和y轴马达不旋转、z轴旋转卡簧易脱落、加热器损坏等常见故障进行快速处理的方法,结果可为同型号扫描电镜科研人员提供维修与维护方面的参考。

基于FMEA的ESAD测试系统设计

针对当前引信电子安全系统(ESAD)测试效率低、无法定位故障点和原因、不便于后续修复改善的问题,设计了一种基于典型引信故障失效模式与影响分析(FMEA)的ESAD测试系统。根据FMEA方法分析得出引信故障信息表,通过I/O接口与数模转换模块按照上位机中装定的时序输出解除保险所需激励信号并实时监测其状态。根据监测状态判断ESAD是否存在故障,与故障信息表中的故障模式进行比对,将测试结果和对应故障信息展示在上位机中。该测试系统已应用在引信产品中,效果良好。

汽车电子控制系统故障诊断与维修分析

本文通过具体案例分析了汽车电子控制系统的故障原因,并结合该系统的结构与工作原理对故障进行诊断与排除。为汽车维修技师、汽车专业教师从事汽车故障诊断提供有益的参考。

新能源汽车电控系统故障诊断技术分析

随着全球对环境保护和能源危机的关注不断增加,新能源汽车作为一种环保、高效的交通工具正逐渐引起了人们的关注。作为国内最大的新能源汽车制造商之一,新能源汽车公司在电动汽车的研发与生产上取得了显著的进展。为了确保新能源汽车的顺利运行,电控系统的结构和故障诊断技术至关重要。本论文将对新能源汽车电控系统结构及故障诊断技术进行详细论述。