A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

Robust Optimum Design of Thrust Hydrodynamic Bearings for Hard Disk Drives

This paper describes the robust optimum design which combines the geometrical optimization method proposed by Hashimoto and statistical method. Recently, 2.5″ hard disk drives (HDDs) are widely used for mobile devices such as laptops, video cameras and car navigation systems. In mobile applications, high durability towards external vibrations and shocks are essentials to the bearings of HDD spindle motor. In addition, the bearing characteristics are influenced by manufacturing error because of small size of the bearings of HDD. In this paper, the geometrical optimization is carried out to maximize the bearing stiffness using sequential quadratic programming to improve vibration characteristics. Additionally, the bearing stiffness is analyzed considering dimensional tolerance of the bearing using statistical method. The dimensional tolerance is assumed to distribute according to the Gaussian distribution, and then the bearing stiffness is estimated by combining the expectation and standard deviation. As a result, in the robust optimum design, new groove geometry of bearing can be obtained in which the bearing stiffness is four times higher than the stiffness of conventional spiral groove bearing. Moreover, the bearing has lower variability compared with the result of optimum design neglecting dimensional tolerance.

A Study on the Stability Analysis and Optimum Design for Journal Bearings

Based on the establishment of a differential equation of motion and the solution of the oil film whirling locus of the Journal center, a method of judging the stability of bearing is presented. Meanwhile a method of bearing parameter optimization for the optimal stability is recommended. Finally, a short journal bearing is given as an example to illustrate the reasonableness and feasibility of the method.

Optimal Design of Tapered Roller Bearings Based on Multi⁃Physics Objectives Using Evolutionary Algorithms

Rolling element bearing is the most common machine element in rotating machinery.An extended life is among the foremost imperative standards in the optimal design of rolling element bearings,which confide on the fatigue failure,wear,and thermal conditions of bearings.To fill the gap,in the current work,all three objectives of a tapered roller bearing have been innovatively considered respectively,which are the dynamic capacity,elasto-hydrodynamic lubrication(EHL)minimum film⁃thickness,and maximum bearing temperature.These objective function formulations are presented,associated design variables are identified,and constraints are discussed.To solve complex non⁃linear constrained optimization formulations,a best⁃practice design procedure was investigated using the Artificial Bee Colony(ABC)algorithms.A sensitivity analysis of several geometric design variables was conducted to observe the difference in all three objectives.An excellent enhancement was found in the bearing designs that have been optimized as compared with bearing standards and previously published works.The present study will definitely add to the present experience based design followed in bearing industries to save time and obtain assessment of bearing performance before manufacturing.To verify the improvement,an experimental investigation is worthwhile conducting.

IMPROVEMENTS ON THE CONFIGURATION AND OPTIMUM PARAMETERS OF HIGH一SPEED THRUST BEARINGS

It is expounded that h2/ is the Reynolds number form of thrust bearings and effects of the centrifugal forees can be judged by its magnitude. Three methods improving the configuration of pads are given out to overcome the haimful influence of the centrifugal forees on the performance of bearings, and the optimized parameters for three kinds of high一speed thrust bearings are provi- ded by solving the varying一viscositied three一dimensional Reynolds equation.

Seismic response of selective pallet racks isolated with friction pendulum bearing system

The concept of base isolation for storage racks is still a developing subject and has not been addressed in the recently updated seismic codes for storage racks.The friction pendulum bearing system(FPS)generates a natural period independent of the structure's seismic mass.This property makes FPS an ideal choice for isolating rack structures since merchandise may be placed on the racks in several possible arrangements.The results of a comprehensive parametric study aimed at evaluating the seismic performance of a four-shelf,two-bay selective pallet rack isolated with FPS is presented in this paper.The influence of radius of curvature of the FPS,seismic mass,and mass irregularity on the rack's seismic response is examined.The effect of variation of the coefficient of friction due to axial loading is studied by choosing friction values from two distinct friction characterization studies.The coefficient of friction calculated from these studies shows mild and significant variations,respectively,for the whole range of static axial loads expected on the isolator,and from different pallet mass arrangements considered.The optimum radius of curvature and the appropriate range of friction coefficient,for the defined range of static axial loads,to attain a desirable seismic performance are determined.

Optimal Placement of Reclosers to Minimize Power Losses during Non-Load Bearing Disturbances in a Power Distribution Using Firefly Algorithm

The research reported in this paper focuses on non-technical power loss reduction for power distribution systems. Such reduction of costs of energy not served (ENS.COST), is intelligently evaluated and optimized using a firefly algorithm, from where savings of 43.3% on energy not served are achieved.

Grain Size Influence on the Compaction Aptitude and the Bearing Strength of the Gravel Lateritic Soils

This study is carried out to appreciate the coarse elements influence of the gravel lateritic soils on the pavement behavior. The material involves not only the 0/20 mm granular particles considered in the specifications and on the laboratory tests, but also all of the material that is actually used in the construction sites. Geotechnical characterization was leaned on five differentiated granular classes of 0/20;0/25;0/31.5;0/40 and 0/63 mm diameters. The sample of 0/40 mm gets the best compaction aptitude, while that of 0/31.5 mm reaches the greatest bearing strength and a good Optimum Moisture Content. According to the requirements and its bearing strengths, the material is usable up to subbase layer and its behavior depends on the grain size.

Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

Selective enrichment of TiO_2 and precipitation behavior of perovskite phase in titania bearing slag

The effects of additive agents and growth behavior of perovskite phase as well as temperature change of slag at semi industry scale test were studied. The results show that the increase of steel slag does good to titania enrichment, however, it isn’t useful for the growth and coarsening of the perovskite phase. The additive Si-Fe powder can promote titania enrichment and make perovskite phase grow up easily. While air is blown into the molten slag, the reduced components in slag are oxidized and the released heat raises the temperature of slag.

Submicro-battery effect and selective bio-oxidation model of gold-bearing arsenopyrite by Thiobacillus ferrooxidans

Through the study by electronic probe it was found that many new cracks and holes appear on the surface of gold bearing arsenopyrite crystal oxidized by Thiobacillus ferrooxidans, which are along with some directions. Then the selective bio oxidation model of gold bearing arsenopyrite was set up. The selective bio oxidation resulting from the submicro battery effect of gold/ arsenopyrite mineral pairs naturally forms in the gold bearing arsenopyrite crystal. Thiobacillus ferrooxidans has priority to oxidize the place of gold rich and oxidizes selectedly along with the crystal border, crystal face and crack. The bacteria oxidation process of gold bearing arsenopyrite is divided into three stages: the first stage is the surface oxidation, the second stage is restraining oxidation and the third stage is the filament oxidation, bacteria oxidize along with cracks of arsenopyrite.

Genetic algorithm for pareto optimum-based route selection

A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.

三星堆古蜀文化遗址博物馆大型螺旋坡道结构设计与分析

三星堆古蜀文化遗址博物馆中庭的螺旋坡道采用钢箱梁结构体系,最大半径28.7m,水平投影长度118.5m。介绍了大型螺旋坡道结构体系选型和边界条件的对比过程,采用通用计算软件MIDAS Gen建立多尺度整体模型考虑螺旋坡道和主体结构的相互作用,通过静力分析和反应谱分析考察螺旋坡道在不同荷载作用下的承载能力和变形,并采用有限元软件ANSYS建立分析模型进行连续倒塌分析。结果表明,螺旋坡道采用钢箱梁结构具有较好的整体刚度,静力荷载和地震作用下坡道的应力和变形均满足规范要求,并具有较好的抗连续倒塌能力。

第三代轮毂轴承保证轴向游隙的动态选配方法

针对保证第三代轮毂轴承完成自动装配后的轴向游隙在规定范围的问题,首先根据装配线上轮毂轴承的实际测量推导出轴向游隙公式;其次,以满足轮毂轴承单元轴向游隙为目标,把增加的工艺生产功能转换为约束条件;最后,通过仿真模拟装配机床的合套过程,采用动态路径规划算法获取高精度组件的自动选择性装配方法,实现轮毂轴承钢球与外圈、内圈、法兰自动选配的最优合套方案。该方法提高了轴承轴套率,降低零件的报废率,可进一步推广到分析其他类型的轴承合套问题。

基于最优频段循环脉冲指数谱的轴承故障诊断方法

针对滚动轴承多故障冲击共振频带的非一致性及相互交叠影响问题,提出了一种基于循环脉冲指数(CPI)谱的轴承多故障同步诊断方法。该方法引入短时脉冲峰值矩的变异系数对轴承故障冲击进行量化表征,结合冗余提升小波包对轴承故障信号进行频带塔式分解与频带信号CPI计算,构建了故障信号的CPI比值谱图(CPIRgram);根据CPI比值最大原则对轴承故障信号的最优共振频带进行自适应选择,并采用最优频段循环脉冲谱对轴承各故障特征频率进行了统一表征。仿真与故障试验分析结果表明,本文方法无需故障先验知识与分解参数的优化设置,在强噪声及随机瞬态干扰情况下,也能够准确地对多故障特征频率进行同步检测,检测出的故障频率与其理论值误差均小于1.6 Hz,且对故障冲击强度大小及冲击模式变化具有较好的鲁棒性,有较好的应用前景。

基于耦合谐振及新型空间全向耦合机构的无线电能传输系统

针对传统无线电能传输系统中发射机构和接收机构相对位置发生偏移时,传输功率和效率急剧下降的问题,提出了一种基于耦合谐振及新型空间全向耦合机构的无线电能传输系统。首先通过有限元仿真分析发射机构的磁场分布特性,设计了适应其磁场分布的接收机构;然后基于串串谐振补偿结构建立系统数学模型,分析系统传输特性;在此基础上提出了最优输出方式选择方法和传输特性优化方法。最后,通过试验验证了所提系统多自由度传输能量的可行性和有效性。试验结果表明,接收机构在多角度情况下的输出功率维持在35 W以上,传输效率稳定在60%~75%。

某不规则多层长途汽车站结构设计及分析

某不规则多层长途汽车站工程主要由长途客运站、公交首末站、换乘连廊及站台雨棚组成。长途客运站主体结构采用框架-剪力墙结构+张弦梁屋盖。换乘连廊采用单排柱柱顶铰接的排架结构。主要介绍了具有多排斜柱、扭转不规则、楼板大开洞、凹凸不规则等多项不规则的长途客运站的抗震性能设计、屋面张弦梁结构设计;分析了钢箱梁+钢筋混凝土Y形分叉柱的换乘连廊的结构特点;剖析了钢箱梁考虑剪力滞效应和受压局部稳定影响的有效截面宽度的计算及Y形分叉柱与横梁、Y形分叉柱柱墩支座选型的设计要点。分析结果表明,所采取的合理结构设计确保了结构安全。

基于自适应特征选择k子凸包的滚动轴承故障诊断

滚动轴承故障诊断中往往将特征选择和分类器的设计分别进行研究,从而难以获得满意的分类精度。将特征选择和分类器寻优结合起来,提出了一种自适应特征选择k子凸包(Adaptive Feature Selection K-sub Convex Hull, AFSKCH)的分类模型,从而实现了故障特征自适应选择和分类的一体化。首先,利用凸包距离函数保持数据流形上的局部邻域结构,通过交替构造k子凸包得到特征权值矩阵;其次,采用线性规划接近度方法求解k子凸包距离,利用乘子交替方向法得到自适应特征空间;最后,根据测试点到k子凸包的最小重构距离进行分类。滚动轴承故障振动信号分析结果表明,该方法特征选择性能优于其他特征选择方法,且具有较高的分类精度。

基于混合式特征选择的滚动轴承故障诊断方法

为降低滚动轴承故障特征集的维数,提升诊断精度,提出一种混合式特征选择方法。该方法由两个阶段构成,首先通过费舍尔分值法对原始特征集进行预排序,根据特征的费舍尔得分按照降序排序,利用得分曲线的拐点确定预选子集的范围,去除原始特征集中的无关特征;然后将遗传算法嵌入Wrapper阶段中,利用分类器的识别精度作为评价标准,从预选子集中去除冗余特征,确定最优子集。通过实验证明,该方法可以有效地用于滚动轴承不同故障类型和不同故障程度的诊断,最优子集在仅保留了关键特征的同时,识别精度得到提升。

基于模态筛选的滚动轴承微小故障诊断研究

滚动轴承的自转、部件相互作用和强干扰使测量的振动信号复杂,而滚动轴承的早期损伤类故障特征信息弱小,导致故障特征难以有效提取从而影响故障诊断,因此提出一种敏感模态筛选方法来提高故障诊断的正确性。采用变分模态分解算法对信号进行自适应分解,提取信号的局部特征。基于排列熵的不变性和对冲击信号的敏感性,提出故障因子的敏感模态筛选方法对信号进行重构,抑制振动信号中的干扰和噪声。通过仿真和实验对所提方法进行了验证,结果表明,相比其他处理方法,所提方法信噪比提高了60.5%,误差降低了52.6%,同时故障诊断正确率提高了24.3%,说明了该方法的有效性。