【标题】Centrifugal Pumps & Pump Standards
【参考中译】离心机泵和泵标准
【类型】 期刊
【作者】 Amy Hyde
【摘要】 Q | How does a centrifugal pump work? A centrifugal pump is the most common pump type within the rotodynamic pump family. Centrifugal pumps have a rotating impeller. The liquid enters in line with the shaft and exits the impeller perpendicular(or radial) to the shaft (Image 1, left side). Image 2 is a 180-degree cutaway of a centrifugal pump drawing and shows the impeller enclosed in a casing with inlet and outlet connections. The casing is designed to direct the liquid into the entrance of the impeller and efficiently collects and directs the liquid exiting the impeller into the system piping. The impeller is driven by a shaft, and bearings carry the radial and axial loads. Lastly, there is a dynamic shaft seal that limits leakage along the rotating shaft. It is attached to a shaft that extends through the power frame.
【参考中译】 Q|离心机如何工作？离心机泵是旋转动力泵系列中最常见的泵类型。离心机泵有一个旋转的推进器。液体与轴成一直线进入，并垂直（或放射状）离开轴（图1，左侧）。图2是离心机泵图纸的180度剖面图，显示了封闭在带有入口和出口连接的外壳中的推进器。该外壳旨在将液体引导到推进器的入口，并有效地收集并引导离开推进器的液体进入系统管道。推进器由轴驱动，轴承承受轴向和轴向负载。最后，还有一个动态轴密封，可以限制沿旋转轴的泄漏。它连接到延伸穿过动力框架的轴上。
【来源】 Pumps & Systems 2024, vol.32, no.5
【入库时间】 2024/10/9
【标题】What Are Thermoplastic Valves?
【参考中译】什么是热塑料阀门？
【作者】 EDWINA MERIN JOHNS
【摘要】 The flexibility of this equipment offers potential sustainability and efficiency benefits. In a fluid process system, selecting the right materials for critical components, like valves, is crucial for ensuring optimal performance and longevity. While metal valves have been the standard for decades, thermoplastic valves present a compelling alternative. This article explores the emergence of thermoplastic valves, their potential in fluid control systems and two realworld application case studies where thermoplastic valves replaced failing metallic valves. Thermoplastic Valve Properties. Thermoplastics have garnered attention across industries for their ability to soften under heat and harden upon cooling. This characteristic has led to a surge in the use of thermoplastic valves and has been further driven by their versatility, durability and performance.
【参考中译】 该设备的灵活性提供了潜在的可持续性和效率优势。在流体过程系统中，为阀门等关键部件选择正确的材料对于确保最佳性能和寿命至关重要。虽然金属阀门几十年来一直是标准阀门，但热塑性阀门提供了一种引人注目的替代方案。本文探讨了热塑性阀门的出现、其在流体控制系统中的潜力以及两个现实应用案例研究，其中热塑性阀门取代了失效的金属阀门。热塑性阀门性能。热塑性塑料因其受热软化和冷却硬化的能力而受到各个行业的关注。这一特点导致了热塑性阀门的使用激增，并进一步受到其多功能性、耐用性和性能的推动。
【标题】MICRONEL MEDICAL BLOWERS
【参考中译】MICRONEL医疗鼓风机
【作者】 Rachael Morling
【摘要】 Micronel Medical Blowers are used in medical, laboratory and scientific equipment in a wide range of air movement applications. Micronel high pressure medical blowers and turbine products are used in a wide variety of medical and scientific equipment requiring precise air movement of some kind. Applications such as Breathing Therapy, Ventilators, Respirators, Personal Protection, ICU Air Mattresses, Air Purification, Disinfection, Air Sampling, Laboratory, Diagnostic & Test all require some form of air movement with significant pressure or suction (vacuum) to force air through tubes or chambers or overcome system/filter resistance. Micronel's high performance blower range is perfectly tailored to the needs and requirements of medical breathing ventilation therapy. Full Intensive care unit (ICU) intubated ventilator systems may require over 60+ millibar pressure to fill and inflate diseased lungs. For less severe cases a less harsh treatment may be used, with a tight-fitting mask to provide so called continuous positive airway pressure (CPAP), as an alternative non-invasive method of ventilator support for some patients. CPAP ventilators typically require around 20 to 30 millibars pressure to assist with breathing and oxygen therapy. Micronel's medical blowers and turbines utilise long life, highly efficient, highspeed brushless motor drives in the smallest form factors to generate high static pressures up to 16 mbar with up to 1000 l/min free blowing airflow. Crucially, versions with Oxygen O_2 and gas resistance are also available.
【参考中译】 Micronel医用鼓风机广泛应用于医疗、实验室和科学设备中的空气流动应用。Micronel高压医用鼓风机和涡轮机产品广泛应用于需要某种精确气流的医疗和科学设备中。呼吸疗法、呼吸机、呼吸器、个人防护、ICU空气床垫、空气净化、消毒、空气采样、实验室、诊断和测试等应用都需要某种形式的空气流动，具有显著的压力或吸力(真空)，以迫使空气通过管道或腔室或克服系统/过滤器阻力。美光S高性能风机系列是针对医用呼吸机治疗的需要和要求而完美定制的。全重症监护病房(ICU)插管的呼吸机系统可能需要超过60毫巴的压力才能填充和充气患病的肺。对于不太严重的病例，可以使用不那么苛刻的治疗，并使用紧贴的口罩来提供所谓的持续气道正压(CPAP)，作为对一些患者进行呼吸机支持的替代非侵入性方法。CPAP呼吸机通常需要大约20到30毫巴的压力来辅助呼吸和氧疗。S医用鼓风机和涡轮机采用寿命长、效率高的高速无刷电机驱动器，在最小的外形尺寸中产生高达16mbar的高静压和高达1000 L/分钟的自由吹风。关键是，也有氧气和气体阻力的版本可供选择。
【来源】 Design Solutions 2024, no.Mar.
【标题】Transfer Learning-Based Fault Diagnosis of Single-Stage Single-Acting Air Compressor
【参考中译】基于传递学习的单级单作用空气压缩机故障诊断
【关键词】 Air compressor; Deep learning; Pre-trained models; AlexNet; GoogLeNet; ResNet50; VGG19; VGG16
【参考中译】 空气压缩机;深度学习;预训练模型; AlexNet; GoogLeNet; ResNet 50; VGG 19; VGG 16
【作者】 G. Chakrapani; S. Naveen Venkatesh; S. Aravinth; V. Sugumaran
【摘要】 Introduction Reciprocating air compressor, which is also known as piston compressor is one of the crucial machinery used in various production lines to move gas at high pressure. Research question The prolonged operation of this machine can lead to internal damage. Therefore, it is highly important to incorporate fault diagnosis to prevent sudden and unforeseen failure. Condition monitoring and fault diagnosis of machines are becoming more and more crucial in various industries. They have special importance in places where the breakdown of machines can cause a tremendous financial crisis. Although there are extensive research works in this area, fault diagnosis of reciprocating air compressors using deep learning is still unexplored. Methodology In this paper, the condition monitoring of air compressors is discussed using deep learning methods. First, different modes of signal acquisition are surveyed, and the best one among them is chosen. Out of several faults, five significant faults that are prone to air compressors are taken into study. Magnitudes of vibration signals are captured using the accelerometer sensor. These signals are converted to plots using MATLAB and the faults are classified using pretrained networks like AlexNet, GoogLeNet, ResNet50, VGG19 and VGG16. Results The results obtained show that the AlexNet pretrained network exhibits the best fault classification rate of 100% in a minimum computational time of 570 s.
【参考中译】 介绍往复式空压机，又称活塞式空压机，是各种生产线上用于高压输送气体的关键机械之一。研究问题这台机器长时间运行会导致内部损坏。因此，结合故障诊断以防止突发和不可预见的故障是非常重要的。机械设备的状态监测和故障诊断在各个行业中变得越来越重要。在机器故障可能导致巨大金融危机的地方，它们具有特别重要的意义。虽然在这方面已经有了大量的研究工作，但基于深度学习的往复式空压机故障诊断还没有得到深入的探索。本文采用深度学习方法对空压机的状态监测进行了研究。首先，对不同的信号采集方式进行了比较，从中选出了最优的一种。在几个故障中，研究了空气压缩机易发生的五个重大故障。使用加速度计传感器捕获振动信号的大小。这些信号使用MatLab转换成曲线图，并使用AlexNet、GoogLeNet、ResNet50、VGG19和VGG16等预先训练的网络对故障进行分类。结果在最小计算时间为570 S的情况下，AlexNet网络的故障分类效果最好，分类正确率为100%。
【来源】 Journal of Vibration Engineering & Technologies 2024, vol.12, no.3 Pt.2
【标题】CABINET COOLER SYSTEMS
【参考中译】橱柜冷却器系统
【作者】 Ellen Parson
【摘要】 The EXAIR NEMA 4 cabinet cooler systems (available in various cooling capacities as well as NEMA 12, NEMA 4X, and hazardous location models)are constructed from durable aluminum and designed to cool dust-tight, oiltight, splash-resistant, indoor/outdoor enclosures. This cooling method can be installed in minutes through a standard electrical knockout. They provide a low-cost alternative to other cooling methods, such as fans, heat exchangers, and refrigerant air conditioners. They are CE compliant and UL and ULC listed.
【参考中译】 EXair EMA 4橱柜冷却器系统（提供各种冷却能力以及EMA 12、EMA 4X和危险场所型号）由耐用铝制成，旨在冷却不漏尘、不漏油、防溅的室内/室外外壳。这种冷却方法可以通过标准的电气敲出在几分钟内安装。它们为其他冷却方法（例如风扇、热交换器和制冷剂空调）提供了低成本的替代方案。它们符合CE标准，并获得UL和ULC认证。
【来源】 EC&M 2024, vol.123, no.4
【标题】Gearbox Fault Diagnosis Using REMD, EO and Machine Learning Classifiers
【参考中译】使用REMD、EO和机器学习分类器进行变速箱故障诊断
【关键词】 Fault diagnosis; Gearbox; Feature extraction; Feature selection; Feature classification; Vibration signals
【参考中译】 故障诊断;齿轮箱;特征提取;特征选择;特征分类;振动信号
【作者】 Adel Afia; Fawzi Gougam; Chemseddine Rahmoune; Walid Touzout; Hand Ouelmokhtar; Djamel Benazzouz
【摘要】 Gearboxes are critical equipment in many industrial applications such as machine manufacturing, petrochemical industry, renewable energy, etc. However, due to their complex structure and regularly harsh working environment, gearboxes are inevitably prone to a variety of faults and defects during operation. Therefore, intelligent condition monitoring techniques are crucially important for early gear and bearing fault recognition and detection to avoid any industrial failure due to machine breakdowns. In this paper, an intelligent algorithm for gear and bearing fault diagnosis is suggested based on several approaches mainly: robust empirical mode decomposition (REMD), time domain features are used for the feature extraction step, while equilibrium optimizer (EO) in the feature selection. For feature classification, random forest (RF), ensemble tree (ET) and nearest neighbors (KNN) are chosen as classifiers. REMD is used to alleviate the mode mixing problem by monitoring the sifting process and selecting the optimal iteration number. EO is a recent optimization approach based on the laws of physical theory in nature. EO reduces the high-dimensional data problem, by filtering redundant features, and increasing model generalization efficiency by avoiding the over-fitting curse. The proposed approach is applied to real-time vibration signals from a healthy gearbox and four different faulty gear and bearing conditions. According to our approach, data signals are decomposed by REMD to several intrinsic mode functions (IMFs). Thereafter, time-domain features are computed for each IMF to construct the feature matrix for every gear and bearing health status. After that, EO is applied to every matrix in the feature selection step. Finally, RF, ET and KNN are used to calculate classification accuracy and give the confusion matrix. Compared to several feature selection techniques, experimental results prove the efficiency of the proposed approach in detecting, identifying, and classifying all gear and bearing defects even under different operating modes.
【参考中译】 变速箱是机械制造、石油化工、可再生能源等许多工业应用中的关键设备。然而，由于变速箱结构复杂，工作环境恶劣，在运行过程中不可避免地会出现各种故障和缺陷。因此，智能状态监测技术对于齿轮和轴承故障的早期识别和检测至关重要，以避免因机器故障而导致的任何工业故障。本文提出了一种基于几种方法的齿轮轴承故障智能诊断算法：稳健经验模式分解(REMD)，特征提取采用时域特征，特征选择采用平衡优化器(EO)。对于特征分类，选择随机森林(RF)、集成树(ET)和最近邻(KNN)作为分类器。REMD通过监测筛选过程和选择最优迭代次数来缓解模式混合问题。EO是一种基于自然界物理理论定律的最新优化方法。EO通过过滤冗余特征来减少高维数据问题，并通过避免过拟合诅咒来提高模型泛化效率。将该方法应用于一个健康的齿轮箱和四个不同故障齿轮和轴承状态的实时振动信号。根据我们的方法，数据信号被REMD分解成几个固有模式函数(IMF)。之后，计算每个IMF的时域特征，以构造每个齿轮和轴承健康状态的特征矩阵。在此之后，在特征选择步骤中对每个矩阵应用EO。最后，使用RF、ET和KNN计算分类精度，并给出混淆矩阵。实验结果表明，与几种特征选择方法相比，即使在不同的运行模式下，该方法也能有效地检测、识别和分类所有的齿轮和轴承缺陷。