DH型离心空压机旋转失速故障诊断及治理

旋转失速是压缩机的常见故障 ,但诊断该故障时易和其他故障相混淆。本文从旋转失速的振动机理出发 ,归纳了旋转失速的故障识别特征。旋转失速的特征频率是成对出现的 ,随流量的减少而降低。该故障特征在现场诊断时得到验证 ,并成功应用于现场诊断     

Highly Functional TNTs with Superb Photocatalytic,Optical, and Electronic Performance Achieving Record PV Efficiency of 10.1% for 1D‐Based DSSCs

Different nanostructures of TiO₂ play an important role in the photocatalytic and photoelectronic applications. TiO₂ nanotubes (TNTs) have received increasing attention for these applications due to their unique physicochemical properties. Focusing on highly functional TNTs (HF‐TNTs) for photocatalytic and photoelectronic applications, this study describes the facile hydrothermal synthesis of HF‐TNTs by using commercial and cheaper materials for cost‐effective manufacturing. To prove the functionality and applicability, these TNTs are used as scattering structure in dye‐sensitized solar cells (DSSCs). Photocatalytic, optical, Brunauer‐Emmett‐Teller (BET), electrochemical impedance spectrum, incident‐photon‐to‐current efficiency, and intensity‐modulated photocurrent spectroscopy/intensity‐modulated photovoltage spectroscopy characterizations are proving the functionality of HF‐TNTs for DSSCs. HF‐TNTs show 50% higher photocatalytic degradation rate and also 68% higher dye loading ability than conventional TNTs (C‐TNTs). The DSSCs having HF‐TNT and its composite‐based multifunctional overlayer show effective light absorption, outstanding light scattering, lower interfacial resistance, longer electron lifetime, rapid electron transfer, and improved diffusion length, and consequently, J _SC, quantum efficiency, and record photoconversion efficiency of 10.1% using commercial N‐719 dye is achieved, for 1D‐based DSSCs. These new and highly functional TNTs will be a concrete fundamental background toward the development of more functional applications in fuel cells, dye‐sensitized solar cells, Li‐ion batteries, photocatalysis process, ion‐exchange/adsorption process, and photoelectrochemical devices.     

基于PSO优化BP的冷水机组故障诊断研究

本文将PSO(粒子群算法)优化BP(误差反向传播神经网络)应用于离心式冷水机组的故障诊断,针对7种典型故障,包括4种局部故障与3种系统故障,建立了PSO优化BP的诊断模型。结果表明:PSO优化后的BP神经网络(包括单隐层与双隐层)故障诊断性能显著提高,神经网络结构简化,较少的隐含层节点即可获得较优的诊断性能。单隐层神经网络优化后最佳隐含层节点数从18降至10,诊断正确率从89. 42%提升至95. 30%;双隐层神经网络优化后最佳隐含层节点数从25降至12,诊断正确率从97. 87%提升至98. 11%,诊断用时仅为优化前的23%。故障诊断虚警率(假报及漏报)降低,且显著改善了系统故障尤其制冷剂泄漏故障的诊断性能,对正常情况的识别率亦极大提高。PSO优化有助于BP网络跳出局部极小值,较好地改善了故障诊断性能。     

半监督极限学习机用于Android手机活动识别的研究

基于对现有Android手机活动识别技术的分析,针对从不完全、不充分的移动传感器数据中推断人体活动的难题,将能根据无标签样本提高识别预测准确性和速度的半监督(SS)学习和体现模式分类回归的有效学习机制的极限学习机(ELM)相结合给出了解决Android手机平台的人体活动识别问题的半监督极限学习机(SS-ELM)方法,并进一步提出了主成分分析(PCA)和半监督极限学习机(SS-ELM)结合的PCA+SS-ELM新方法。实验结果表明,该方法对人体活动的识别正确率能达到95%,优于最近提出的混合专家半监督模型的正确率,从而验证了该新方法是可行性。     

稀土离子掺杂改性TiO2光催化剂

TiO2较宽的禁带宽度和低的量子转换效率限制了其实际的应用,对TiO2进行改性以克服上述两方面问题一直是光催化领域研究的重点。稀土元素因其独有的电子结构和光学性质,在离子掺杂改性TiO2研究中受到重点关注。主要介绍了稀土离子掺杂改性TiO2所形成杂质能级的位置对改善其光催化性能的作用机理;综述了稀土离子掺杂对TiO2的晶型、晶粒尺寸和光谱吸收的影响;最后提出了目前研究存在的问题及发展趋势。     

基于柯氏熵的汽车发动机状态预测的可行性研究

由于非线性系统存在长期不可预测性，基于此，建立了基于时间序列的柯尔莫哥洛夫熵求解方法，并利用柯尔莫哥洛夫熵求取了复杂机械系统状态的最大可预测的点数。这一研究为确立复杂设备状态预测的可信区间提供了理论支持，所获得的方法为复杂机械系统状态预测的可预测时间的界定提供了行之有效的技术方法。     

Wakefulness in young and elderly subjects driving at night in a car simulator

Young drivers are over-represented in nighttime traffic accidents and several studies have suggested that many accidents are associated with elevated sleepiness levels. It has been suggested that there may be a connection between lowered wake capacity and functional sensory motor skills on the one hand and sleep deprivation at the circadian low in young drivers on the other.Performance during a 45/min evening and night drive among young (n = 10, age range 18–24 years) and elderly (n = 10, age range 55–64 years) subjects was studied using a moving base driving simulator. EEG was measured continuously. Every 5 min, subjects were rated on the Karolinska Sleepiness Scale (KSS). Saliva cortisol was assessed before and after each drive.The results showed that sleepiness increased across each drive and was higher among young drivers at night. Relative EEG power increased among older drivers for frequencies of 10–16 Hz. The sigma 1 frequency band (12–14 Hz) proved particularly sensitive to sustained driving, and was elevated among subjects in the elderly group. Cortisol levels before and after the evening and night drive showed higher mean levels for elderly subjects.The present study has demonstrated that young drivers were more sleepy while driving at night. The effects could represent a mobilization of effort and a reorganization of brain firing pattern among older subjects, possibly reflecting better ability and effort to resist sleepiness.     

基于小波时频框架分解方法的滚动轴承故障诊断

损伤点通过其它元件时引起的周期性冲击是判断滚动轴承局部损伤故障的关键特征信息.针对滚动轴承的振动特点,设计了小波时频框架,利用框架分解方法在匹配信号特征结构,直接提取特征信息方面的优势,分析了滚动轴承的振动信号.根据框架分解结果,在时频联合域内清晰直观地提取了滚动轴承局部损伤故障的周期性冲击特征,识别了滚动体、内圈和外圈的单点缺陷,与小波变换的对比验证了框架分解在检测滚动轴承局部损伤故障方面的有效性.     

变载荷齿轮箱故障信号智能检测方法

针对变载荷激励下齿轮故障信号检测和故障识别的问题,提出一种基于经验模态分解和深度信念网络的变载荷齿轮箱故障信号智能检测方法。首先通过经验模态分解方法将非平稳的加速度振动信号分解成若干平稳的本征模态函数;选出啮合频率及倍频所在的本征模态函数,重构信号,求出重构信号的频谱,作为深度信念网络的输入;深度信念网络通过对输入频谱进行预训练和特征学习,并建立变载荷激励下齿轮故障识别分类模型;最后,用构建好的深度信念网络对测试样本进行故障诊断。试验结果表明,提出的方法能有效地检测和识别变载荷激励下齿轮故障。     

四边简支矩形板损伤诊断

本文建立了四边简支矩形板的固有频率与裂纹长度及裂纹位置关系的人工神经网络模型,该模型可用于结构的损伤监测,预报故障的发生。