Thermal Error Compensation of the Wear-Depth Real-Time Detecting of Self-Lubricating Spherical Plain Bearings

The spherical plain bearing test bench is a necessary detecting equipment in the research process of self?lubricating spherical plain bearings. The varying environmental temperatures cause the thermal deformation of the wear?depth detecting system of bearing test benches and then a ect the accuracy of the wear?depth detecting data. However, few researches about the spherical plain bearing test benches can be found with the implementation of the detect?ing error compensation. Based on the self?made modular spherical plain bearing test bench, two main causes of ther?mal errors, the friction heat of bearings and the environmental temperature variation, are analysed. The thermal errors caused by the friction heat of bearings are calculated, and the thermal deformation of the wear?depth detecting sys?tem caused by the varying environmental temperatures is detected. In view of the above results, the environmental temperature variation is the main cause of the two error factors. When the environmental temperatures rise is 10.3 °C, the thermal deformation is approximately 0.01 mm. In addition, the comprehensive compensating model of the thermal error of the wear?depth detecting system is built by multiple linear regression(MLR) and time series analysis. Compared with the detecting data of the thermal errors, the comprehensive compensating model has higher fitting precision, and the maximum residual is only 1 μm. A comprehensive compensating model of the thermal error of the wear?depth detecting system is proposed, which provides a theoretical basis for the improvement of the real?time wear?depth detecting precision of the spherical plain bearing test bench.

The Friction and Wear Properties of the Spherical Plain Bearings with Self-lubricating Composite Liner in Oscillatory Movement

A test method based on the condition simulation and a friction and wear test machine featuring in oscillatory movement were set up for self-lubricating spherical plain bearings (SPB). In the machine the condition parameters such as load, angle and frequency of oscillation and number of test cycles can be properly controlled. The data relating to the tribological properties of the bearing, in terms of friction coefficient, linear wear amount, temperature near friction surface and applied load, can be monitored and recorded simultaneously during test process by a computerized measuring system of the machine. Efforts were made to improve the measurement technology of the friction coefficient in oscillating motion. In result, a well-designed bearing torque mechanism was developed, which could reveal the relation between the friction coefficient and the displacement of oscillating angle in any defined cycle while the curve of friction coefficient vs number of testing cycles was continuously plotted. The tribological properties and service life of four kinds of the bearings, i.e, the sampleⅠ-Ⅳ with different self-lubricating composite liners, including three kinds of polytetrafluoroethylene (PTFE) fiber weave/epoxy resin composite liners and a PTFE plastic/copper grid composite liner, were evaluated by testing, and the wear mechanisms of the liner materials were analyzed.

An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.

Self-Sensing TDR for Bearing Failure Detection of CFRP Laminate Fastener Hole with Particular Reference to the Effect of Fasteners

Carbon fiber reinforced polymer composites (CFRP) have been applied to aerospace and automobile structures. For many CFRP structures, mechanical metallic fasteners are usually adopted. For the fasteners used in internal structures such as a wing box, the damage to the CFRP structures around fastener holes is visually quite difficult to find. A simple method to find the damage around fastener holes is required. In this study a self-sensing time domain reflectometry (TDR) method is newly applied to detect bearing failure around the fastener holes of CFRP structures. A microstrip-line method is generally used to create a transmission line. When the transmission line is mounted near the metallic fasteners, they may affect the impedance of the transmission line. In this study, the effect of distance between the fasteners and the transmission line was numerically investigated using a finite difference time domain analysis method. After finding the appropriate distance, experiments were performed to detect the bearing failure around a fastener hole. The experiments showed the performance of the self-sensing TDR for detecting bearing failure.

Pretreatment and thiosulfate leaching of refractory gold-bearing arsenosulfide concentrates

A hydrometallurgical process for refractory gold-bearing arsenosulfide concentrates at ambient temperature and pressure was presented, including fine grinding with intensified alkali-leaching （FGIAL）, enhanced agitation alkali-leaching （EAAL）, thiosulfate leaching and displacement. Experimental results on a refractory gold concentrate showed that the total consumption of NaOH in alkaline leaching is only 41% of those theoretically calculated under the conditions of full oxidization for the same amount of arse- nides and sulfides transformed into arsenates and sulfates, and 72.3% of gold is synchro-dissoluted by thiosulfate self-generated during alkaline leaching. After alkaline leaching, thiosulfate leaching was carried out for 24 h. The dissolution of gold is increased to 91.9% from 4.6% by cyanide without the pretreatment. The displacement of gold by zinc powder in the solution gets to 99.2%. Due to an amount of thiosulfate self-generated during alkaline leaching, the reagent addition in thiosulfate leaching afterwards is lower than the normal.

One-Step-Installation of Offshore Wind Turbine on Large-Scale Bucket-Top-Bearing Bucket Foundation

In 2010,the first offshore wind turbine with integrated installation was established in Qidong sea area of Jiangsu Province,China,which led to the implementation phase of one-step-installation technique based on the design and construction of large-scale bucket-top-bearing (LSBTB) bucket foundation.The critical technique of LSBTB bucket foundation included self-floating towing,penetration with adjustment of horizontal levelness,removability and one-step-installation.The process of one-step-installation included the prefabrication of LSBTB bucket foundation in onshore construction base,installation and debugging of wind power,overall water transportation of foundation and wind power system,and installation of foundation and offshore wind turbine on the appointed sea area.The cost of one-step-installation technique was about 5 000 Yuan/kW,which was 30%-50% lower than that of the existing technique.The prefabrication of LSBTB bucket foundation took about two months.During the one-step-installation process,the installation and debugging of wind power and overall water transportation need about one to two days in sea area within 35 m depth.After the proposed technique is industrialized,the cost will be further reduced,and the installation capacity is expected to be up to 500 wind turbines per year.

ConvNeXt网络及Stacked BiLSTM-Self-Attention在轴承剩余寿命预测中的应用

在滚动轴承剩余使用寿命预测方面,采用传统方法时存在鲁棒性差、精度低等各种问题。近些年来深度学习的发展为解决这些问题提供了新的思路。为了进一步提高对轴承寿命的预测精度,提出了一种基于ConvNeXt网络、堆叠双向长短时记忆网络(SBiLSTM)和自注意力机制(Self-Attention)的滚动轴承寿命预测方法。首先,采用连续小波变换(CWT)构造了振动信号的时频图,以更好地捕捉信号的时域和频域特征;然后,将得到的时频图输入到构建的ConvNeXt网络中,通过卷积、池化和层归一化等操作,对时频图的关键特征进行了提取;最后,将提取后的特征输入到SBiLSTM-Self-Attention模块中,进一步提取了时序信息和特征权重分配数据,利用PHM2012挑战数据集进行了验证,通过实验分析了该方法的均方根误差(RMSE)和平均绝对误差(MAE)。研究结果表明:相较于现有技术方法,该方法的平均RMSE为0.031;与其他三种方法,即卷积神经网络(CNN)、深度残差双向门控循环单元(DRN-BiGRU)和深度卷积自注意力双向门控循环单元(DCNN-Self-Attention-BiGRU)相比,其平均RMSE值分别下降了79%、74%和55%,MAE值分别下降了78%、73%和53%,说明该方法在滚动轴承剩余寿命预测中有较好的性能。

STUDY ON PERFORMANCE OF COMPOSITE JOURNAL BEARING SEALED AND LUBRICATED WITH FERROFLUID

The theoretical computation model of the composite journal bearing sealed and lubricated with ferrofluid is established The oil film pressure distribution,temperature field and heat conduction equation of the bearing liner are jointly calculated The static and dynamic properties of the bearing under condition of different eccentricities and ratios of width to diameter are obtained and discussed The results show the feasibility of this type of bearing for smaller eccentricities and width to diameter ratios,and that the oil film temperature,which is more subject to be influenced by the rotating speed,is higher than that of the bearing with leakage flow The key problem of the further development and application of the bearing is to design more reasonable and effective types of seal structures

基于DCNN网络及Self-Attention-BiGRU机制的轴承剩余寿命预测

深度神经网络在剩余寿命预测(RUL)领域得到了广泛的应用。传统的滚动轴承寿命预测模型存在预测精确度较低、鲁棒性较弱的问题。为了进一步提升预测模型的精确度以及鲁棒性,提出了一种融合深度卷积神经网络(DCNN)、双向门控循环单元(BiGRU)以及自注意力机制(Self-Attention)三种模块的滚动轴承剩余使用寿命预测模型。首先,利用DCNN网络对原始振动信号的时域特征、频域特征进行了提取;然后,使用不确定量化的方法对提取到的特征进行了评价和筛选,利用筛选过后的特征构建了新的替代特征集;最后,利用Self-Attention-BiGRU网络对轴承的剩余使用寿命进行了预测,并在IEEE PHM2012数据集上进行了验证。实验结果表明:相较于BiGRU、GRU和BiLSTM三种模型的预测结果,基于DCNN及Self-Attention-BiGRU方法的预测结果最优,两项误差值:平均绝对误差(MAE)、均方根误差(RMSE)最低,其中工况一的一号轴承RUL预测的MAE值相较于BiGRU、GRU以及BiLSTM网络分别下降了7.0%、7.4%和6.5%,RMSE值相较于其他三种模型分别下降了7.6%、8.4%和6.9%,预测的Score值最高,分值为0.985。通过不同数据集的划分,证明了该方法在轴承RUL预测时的强鲁棒性。实验结果验证了基于DCNN网络及Self-Attention-BiGRU模型在轴承剩余使用寿命预测中的有效性。

结合LSTM和Self‑Attention的滚动轴承剩余使用寿命预测方法

为了构建准确表征滚动轴承退化过程的趋势性健康度指标,提高滚动轴承剩余使用寿命(Remaining Useful Life,RUL)的预测精度,提出了一种结合长短期记忆(Long‑Short Term Memory,LSTM)和自注意力(Self‑Attention)机制的神经网络模型(LSTM‑SA)用于滚动轴承RUL预测。利用包络解调获得原始信号的包络谱,再将包络谱分段并计算对应频段的皮尔逊相关系数,得到具有单调性和趋势性的退化特征;将退化特征归一化处理后作为LSTM‑SA模型的输入,并利用LSTM自适应提取退化特征时间上的内部相关性以及Self‑Attention对关键信息的筛选,消除无用信息的干扰,挖掘深层次特征,构建健康度指标并得到退化曲线;确定失效阈值,利用最小二乘法拟合退化曲线,预测寿命失效点,实现滚动轴承的RUL预测。在PHM2012数据集上的实验结果表明,所提出的方法相比于其他文献,平均绝对误差分别降低了43.18%,62.57%和59.44%,平均得分分别提高了10.87%,45.71%和34.21%;在工程实际数据中的实验结果表明,所提出方法的平均预测误差分别比Standard‑RNN和CNN方法降低了39.58%和74.86%。

Numerical simulation of the seismic behavior of self-centering steel beam-column connections with bottom flange friction devices

A new type of steel moment resisting frame with bottom flange friction devices （BFFDs） has been developed to provide self-centering capacity and energy dissipation, and to reduce permanent deformations under earthquakes. This paper presents a numerical simulation of self-centering beam-column connections with BFFDs, in which the gap opening/closing at the beam-column interfaces is simulated by using pairs of zero-length elements with compression-only material properties, and the energy dissipation due to friction is simulated by using truss elements with specified hysteretic behavior. In particular, the effect of the friction bolt bearing against the slotted plate in the BFFDs was modeled, so that the increase in lateral force and the loss of friction force due to the bolt bearing could be taken into account. Parallel elastic-perfectly plastic gap （ElasticPPGap） materials in the Open System for Earthquake Engineering Simulation （OpenSees） were used with predefined gaps to specify the sequence that each bolt went into the bearing and the corresponding increase in bending stiffness. The MinMax material in OpenSees is used to specify the minimum and maximum values of strains of the ElasticPPGap materials. To consider the loss of friction force due to bok bearing, a number of parallel hysteretic materials were used, and the failure of these materials in sequence simulated the gradual loss of friction force. Analysis results obtained by using the proposed numerical model are discussed and compared with the test results under cyclic loadings and the seismic loading, respectively.

基于Self-Weight与t-SNE的滚动轴承故障诊断

针对滚动轴承故障信号非线性、故障特征种类繁多难以准确分类的问题,提出了一种Self-Weigh与t-SNE相结合的解决方法。先用WPT完成对原始故障信号的处理及特征的提取,然后采用Self-Weigh评估每个特征的敏感程度,获取最优特征;再对这些最优特征通过t-SNE进行降维可视化处理,获取低维敏感特征,并将其作为AP传播聚类的输入,从而实现故障类型100%正确识别。采用机械综合模拟实验平台的轴承数据加以验证,并与采用t-SNE、Self-Weigh+PCA方法进行对比,结果体现了所提方法的优势。

变速条件下流体动压效应稳健性分析

以油膜作为中间介质,对于高速旋转机械传动系统的可靠性、稳定性的提升有显著优势。而高速轴承-转子系统在加工制造和运行过程中,不可避免地存在影响系统承载性能的不确定性因素,影响油膜形状,导致承载性能发生变化,实际工作性能偏离设计目标。以高速动压油膜为研究对象,为掌握几何参数和转速对油膜承载性能波动的影响,进行稳健优化和分析,提取油膜性能稳健优化结果的特征区域。针对传动系统的不同需求,采用计算流体力学方法求解不同转速下的动压滑动轴承油膜压力场,进而求解其主要性能指标:承载力和摩擦功耗。建立研究目标的Kriging近似模型,并在样本点临近区域选择稳健目标计算的子空间,计算目标均值和方差。利用非支配排序遗传算法(NSGA-II)求解不同目标组合的Pareto最优解集。结合自组织映射图(SOM)方法进行相关性分析,提取设计目标与几何参数、转速之间的相关性特征,分析最优特征区域中偏心率对目标稳健性的影响,最终确定稳健性好的特征区域,并选择个别结果进行仿真计算验证。结果表明:所提出的最优性分析方法能够清晰展现出稳健最优性区域在设计空间中的分布情况,便于降低几何参数和转速的波动对油膜性能的影响;所提方法能提升优化设计结果的可实现性,有效促进理论设计与工程实际的衔接。

十字形钢管束自密实混凝土短肢剪力墙轴压性能研究

为研究十字形钢管束自密实混凝土短肢剪力墙的轴压受力性能,通过模型试验研究和ABAQUS数值模拟相结合的方法,对不同腔体数十字形钢管束自密实混凝土短肢剪力墙的轴压性能进行分析。首先,根据轴压试验结果对剪力墙的破坏形态和轴向荷载-应变曲线等轴压受力性能进行分析,提出轴压承载力计算公式。其次,建立剪力墙轴压有限元计算模型,将试验结果和模型计算结果进行对比以验证有限元模型的准确性。最后,根据有限元参数化分析结果验证并修正剪力墙轴压承载力计算公式。研究发现,剪力墙的轴向荷载-应变曲线在不同腔体数下具有相似的特征,表明其变形能力良好。在破坏过程中,钢管束对核心混凝土的约束作用显著,提高了试件的延性和承载能力,其中钢管束的破坏主要表现为多波鼓曲和焊缝开裂,尤其是在钢管束中部的1/4区域。此外,随着腔体数增加,剪力墙极限承载力提升,但其延性系数有所下降。本研究提出的简化公式与有限元分析结果的误差控制在10%以内,该公式可为工程设计应用提供理论借鉴。

自监督学习结合对抗迁移的跨工况轴承故障诊断

轴承智能故障诊断应用中,由于实际工况复杂多变,极难获得足够的真实故障数据,且目标域和源域信号存在较大差异,导致深度模型的跨工况迁移识别也出现特征提取及分类困难、模型泛化性弱。考虑到目标域存在大量无标签数据,引入无监督思想,提出基于自监督学习结合对抗迁移的改进方法。首先根据信号本身特点创建辅助任务,对大量无标签数据学习,建立源域与目标域故障类别之间的内在联系;再通过对抗域适应和联合最大平均差异将源域知识迁移到目标域中,结合辅助任务优化两域差异,最终实现目标域准确的故障分类。用2个公开的轴承数据集上验证了所提方法的性能,实验结果表明,所提方法的故障诊断识别准确率在多数情况下均高于98%。

石墨烯/多孔聚酰亚胺自润滑保持架材料的制备及其性能

为提高航天长寿命轴承用多孔聚酰亚胺(PPI)保持架材料的自润滑性能,采用机械混合和原位聚合的方式分别制备了石墨烯(GP)改性多孔聚酰亚胺材料,通过扫描电镜、压汞仪、万能拉伸试验机及摩擦试验机等设备对其微孔性能、力学性能以及摩擦学性能进行分析,结果表明:原位聚合能形成“蜂窝状”多孔结构,有利于制备低孔隙率和小孔径的多孔聚酰亚胺材料;石墨烯含量0.5%时,机械混合和原位聚合工艺所制得复合材料的拉伸强度均最高,较未改性PPI分别提高了13.0%和42.3%;石墨烯含量0.5%和1.0%时,原位聚合试样具有更优异的耐磨性,磨损量较未改性PPI分别降低了73.3%和47.8%。

液压泵用推力球轴承疲劳剥落原因

某台液压泵推力球轴承发生疲劳剥落现象。采用宏观观察、化学成分分析、金相检验、硬度测试、压印深度测量等方法对轴承剥落的原因进行分析。结果表明:轴承发生疲劳剥落的原因为,保持架兜孔压印部位变形量大,钢球受到过度约束,钢球与保持架发生运动干涉,最终导致轴承疲劳剥落。采用聚醚醚酮材料制造的自锁保持架替代铜实体保持架,可有效改善推力球轴承疲劳剥落的问题。

一种POA-VMD和自编码器结合的风电机组轴承劣化指标构建及故障诊断方法

针对目前轴承性能劣化指标的构建及故障诊断高度依赖专家经验,限制条件繁多,实际应用情景单一的问题,提出一种鹈鹕优化算法(POA)优化的变分模态分解(VMD)和自编码器结合的风机轴承劣化指标构建及故障诊断方法。首先利用POA-VMD算法将轴承振动信号采用自适应方法分解为K个固有模态分量(IMF),并针对上述分量分别构建K个自编码器;然后以正常状态振动信号的分解结果为训练样本完成自编码器的训练,并以训练完成后模型的输出结果为基础构建轴承劣化指标,借助劣化指标监测轴承早期微弱故障;最后对故障时刻振动信号的IMF分量重构结果进行包络谱分析,确定故障的类型。经实验验证:该方法不仅可以清晰地展现轴承的劣化过程,对早期微弱故障敏感性高,而且在故障发生后可以准确诊断出故障类型。

织物衬垫磨损对自润滑关节轴承动力学的影响

自润滑关节轴承在服役过程中,衬垫的磨损导致自润滑关节轴承内外圈之间产生间隙,自润滑运动副间隙的存在加速了内外圈之间的碰撞以及衬垫的进一步磨损,对自润滑关节轴承的动力学特性产生较大的影响。此外,衬垫的磨损还会导致各构件非线性特性的恶化,降低自润滑关节轴承的稳定性。为研究织物衬垫磨损对自润滑关节轴承动力学响应的影响,文中建立了带间隙的自润滑关节轴承运动副矢量模型。首先,通过修正Lankarani-Nikravesh(L-N)法向接触力模型和改进的库仑摩擦力模型,对内外圈间隙处的碰撞力进行建模;然后,基于牛顿第二定律建立了含间隙的刚柔耦合动力学方程;最后,分析讨论不同织物衬垫磨损量和不同摩擦因数条件下含间隙的自润滑关节轴承传动系统的动力学特性,并利用相图和Poincare映射图分析了自润滑关节轴承的非线性特性。结果表明:随着织物衬垫磨损量的增加,自润滑关节轴承内圈和外圈的动力学行为表现出非线性特性;衬垫磨损量一定时,随着衬垫摩擦因数的增加,系统稳定性得到提高,抑制了混沌现象的发生。

湿陷性黄土场地中单桩承载力与负摩阻力试验研究

为了研究湿陷性黄土场地中土体湿陷引起的桩基承载力问题,在平凉市某工程现场利用机械成孔灌注桩(桩径0.8 m,桩长26 m)进行荷载-特殊浸水试验,对试验过程中不同深度处的桩身轴力和桩侧摩阻力进行分析。结果表明:天然状态下试验桩极限承载力大于13 200 kN,受力特性为典型的摩擦桩;由于前期泥浆护壁施工方式对孔壁的浸水影响以及桩周土沿桩长方向上物理力学性能分布的不确定性,侧摩阻力沿深度出现多个峰值,最大值均出现在泥浆护壁以上(桩身2~6 m)位置处;在渗水孔与渗水坑相结合的特殊浸水试验条件下,桩侧负摩阻力在浸水试验第8 d时趋于稳定,中性点在桩身8 m位置处,负摩阻力为107.6 kPa;试验过程中桩周土沿桩长分段发生湿陷,而不是随浸水自上到下逐渐湿陷;受到预浸水作用的试件SZH在静载试验结束后的中性点出现在桩身12 m位置处,负摩阻力为127.8 kPa,人工浸水试验主要使桩身8 m位置以上桩身轴力增大,中性点位置保持不变,表明负摩阻力受桩顶荷载、浸水方式和浸水固结时间影响显著。