港口门座式起重机回转支承故障分析、维修技巧及预防措施

本文以港口门座式起重机回转支承的故障分析、维修技巧及预防措施为研究对象,通过研究港口门座式起重机的回转支承出现的故障,进而指出相应的维修技巧和预防措施。研究结果表明,在日常使用和维护过程中,严禁超载作业、定期润滑和保养回转支承,以及定期检查和紧固螺栓、保护密封件等都是行之有效的预防措施。此外,还应重视振动和温度监测,及时发现异常情况并采取相应措施。通过本研究的指导和措施的实施,可以有效延长港口门座式起重机回转支承的使用寿命,提高设备的可靠性和安全性。

转子-滑动轴承系统支承松动-碰摩故障动力学行为及评估方法

本文针对转子-滑动轴承系统支承松动-碰摩故障动力学行为进行分析,并提出基于动力学行为非线性度量的转子-轴承系统支承松动状态评估方法.应用非线性短轴承油膜力模型、松动刚度模型、Hertz接触理论等建立了带有支座松动故障的转子系统局部碰摩动力学模型,研究并分析松动-碰摩故障转子系统随支承松动间隙变化的动力学行为规律.提出基于动力学行为非线性度量的转子-滑动轴承松动-碰摩故障下支承松动状态评估方法,采取泰勒展开获得线性近似动力学模型,量化比较非线性模型与线性近似模型动力学行为的差异.建立松动间隙与非线性度之间的对应关系,直观反映松动间隙对系统动力学行为的影响程度,实现对转子-滑动轴承系统支承松动状态的评估.本文的研究可为转子-滑动轴承系统支承松动状态评估提供理论基础和支撑.

预防和控制起重机回转支承故障的策略探究

随着经济的增长,我国机械工业生产项目进入了发展高峰期。在工业项目中,起重机占据非常重要的位置,其安全问题也逐渐引起社会各界的关注。文章针对起重机回转支承故障的产生原因以及影响因素进行了分析,并对预防和控制措施展开了讨论,旨在进一步积极推动整体起重机工业项目的顺利发展。

襟翼支臂开裂失效分析

对襟翼支臂开裂进行分析，证实支臂开裂属多源疲劳开裂。开裂的原因与失效件使用应力水平较高及维护质量和环境条件等因素引起的腐蚀有关；同时对支臂受力状况和工艺进行了分析讨论，提出了一些改进意见。

基于静态小波变换的T型输电线路行波测距方法

针对现有T型输电线路行波测距算法易受行波波速影响的不足,提出一种新的T型输电线路行波测距方法。采用Clarke变换将相电流转换为独立的模电流,对模电流进行静态小波变换(static wavelet transform,SWT)处理,实现各行波浪涌到达各母线端时刻的标定。首先利用首波头到达三端母线的3个初始时刻定义隶属度,给出利用隶属度实现故障支路判别的判据。运用已有的两端测距公式推导三端测距公式,实现T型线路故障点测距。研究了T节点附近的3种可能性故障情况,提出三次测距方法,使T节点附近故障测距问题得到很好的解决。与现有测距方法相比,SWT具有时间不变性,故障点的测距过程中充分利用T型输电线路的三端测量数据,测距公式中不含波速,因此,所提方法具有测距可靠且精度高的特点。ATP/EMTP仿真验证表明,所提T型输电线路行波测距方法简单可行,且不受过渡电阻、故障类型等因素的影响。

Application of DC component to select fault branch in arc suppression coil grounding system

When single phase earth fault occurs in the arc suppression coil grounding system, the amplitude of the transient capacitance current is high and decays fast, but the attenuation of the transient inductance current is much slower. This paper analyses the DC component of fault branch, and has found it is much bigger than that of the normal branches in transient state. All the simulation results obtained from three compensation types, different fault time and different wave cycles show that the DC component of fault branch is much higher than that of those normal branches. These results verify the effectiveness of taking the DC component as the method of fault line selection in the arc suppression coil grounding system.

Predicting the fault-proneness of class hierarchy in object-oriented software using a layered kernel

A novel kernel learning method for object-oriented （00） software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as an elemental software model. A layered kernel is introduced to handle the tree data structure corresponding to the class hierarchy models. This method was vali- dated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experi- ments showed that our approach is very effective in learning structured data and outperforms the traditional support vector learning methods in accurately and correctly predicting the fault-prone class hierarchy model in real-life OO software.

A Roller Bearing Fault Diagnosis Method Based on Improved LMD and SVM

Aiming at the non-stationary feattwes of the roller bearing fault vibration signal, a roller bearing fault diagnosis methtxt based on improved Local Mean Decomposition （LMD） and Support Vector Machine （SVM） is proposed. In this paper, firstly, the wavelet analysis is introduced to the signal decomposition and reconstruction; secondly, the LMD method is used to decompose the recomtnion signal obtained by the wavelet analysis into a ntmaber of Product Ftmctions （PFs） that include main fault characteristics, thus, the initial feattwe vector matrixes could be formed automatically; Thirdly, by applying the Singular Valueition （SVD） techniques to the initial feature vector matrixes, the singular values of the matrixes can be obtained, which can be used as the fault feature vectors of the roller bearing and serve as the input vectors of the SVM classifier; Finally, the recognition results can be obtained from the SVM output. The results of analysis show that the propsed method can be applied to roller beating fault diagnosis effectively.

3-D numerical modelling of Domino failure of hard rock pillars in Fetr6 Chromite Mine, Iran, and comparison with empirical methods

Fetr6 is an underground mine using the stope-and-pillar mining method. As there was some evidence regarding pillar failure in this mine, improving works such as roof support and replacing existing pillars with concrete pillars (CP) were carried out. During the construction of the second CP, in the space between the remaining pillars, one of the pillars failed leading to the progressive failure of other pillars until 4 000 m 2 of mine had collapsed within a few minutes. In this work, this phenomenon is described by applying both numerical and empirical methods and the respective results are compared. The results of numerical modelling are found to be closer to the actual condition than those of the empirical method. Also, a width-to-height (W/H) ratio less than 1, an inadequate support system and the absence of a detailed program for pillar recovery are shown to be the most important causes of the Domino failure in this mine.

Hybrid Support Vector Machines-Based Multi-fault Classification

Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

Fault diagnosis using a probability least squares support vector classification machine

Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.

A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis

Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.

Fault diagnosis method for switch control circuit based on SVM-AdaBoost

In order to realize the fault diagnosis of the control circuit of all-electronic computer interlocking system(ACIS)for railway signals,taking a five-wire switch electronic control module as an research object,we propose a method of selecting the sample set of the basic classifier by roulette method and realizing fault diagnosis by using SVM-AdaBoost.The experimental results show that the proportion of basic classifier samples affects classification accuracy,which reaches the highest when the proportion is 85%.When selecting the sample set of basic classifier by roulette method,the fault diagnosis accuracy is generally higher than that of the maximum weight priority method.When the optimal proportion 85%is taken,the accuracy is highest up to 96.3%.More importantly,this way can better adapt to the critical data and improve the anti-interference ability of the algorithm,and therefore it provides a basis for fault diagnosis of ACIS.

Mechanisms governing the evolution of a long-lived northwest vortex that caused a series of disasters in Northwest China

The northwest vortex(NWV)is a type of mesoscale vortex that appears with a relatively high frequency in Northwest China.To further the understanding of the NWV’s evolution,in this study,the moisture and circulation budgets of a long-lived NWV(~132 h)that appeared in early August 2019 were calculated.This vortex induced a series of torrential rainfall events in Northwest China and Mongolia,which caused severe transmission line faults and urban waterlogging.Synoptic analyses indicate that the NWV was generated in a favorable background environment characterized by notable upper-level divergence and strong mid-level warm advection.The moisture budget shows that the East China Sea and Bohai Sea acted as the main moisture sources for the NWV-associated precipitation,and the water vapor was transported into the rainfall regions mainly by easterly and southeasterly winds.The circulation budget indicates that,during the developing stage,convergence-related vertical stretching was a dominant factor for the NWV’s development;whereas,the vortex’s displacement from regions with stronger cyclonic vorticity to those with weaker cyclonic vorticity mainly decelerated its development.In the decaying stage,divergence-related vertical shrinking and the net export of cyclonic vorticity due to the eddy flow’s transport resulted in the NWV’s dissipation.

Support Vector Machine for mechanical faults classification

Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary clas- sifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for on line diagnosis for mechanical system.