Unconfined compressive strength and failure behaviour of completely weathered granite from a fault zone

Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.

Research on Asymmetric Fault Location of Wind Farm Collection System Based on Compressed Sensing

Aiming at the problem that most of the cables in the power collection systemof offshore wind farms are buried deep in the seabed,whichmakes it difficult to detect faults,this paper proposes a two-step fault location method based on compressed sensing and ranging equation.The first step is to determine the fault zone through compressed sensing,and improve the datameasurement,dictionary design and algorithmreconstruction:Firstly,the phase-locked loop trigonometric functionmethod is used to suppress the spike phenomenon when extracting the fault voltage,so that the extracted voltage valuewillnot have a large error due to the voltage fluctuation.Secondly,theλ-NIM dictionary is designed by using the node impedancematrix and the fault location coefficient to further reduce the influence of pseudo-fault points.Finally,the CoSaMP algorithmis improved with the generalized Jaccard coefficient to improve the reconstruction accuracy.The second step is to use the ranging equation to accurately locate the asymmetric fault of the wind farm collection system on the basis of determining the fault interval.The simulation results show that the proposedmethod ismore accurate than the compressedsensingmethod andimpedancemethod in fault section location and fault location accuracy,the relative error is reduced from 0.75%to 0.4%,and has a certain anti-noise ability.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

Spatial and temporal characteristics of acoustic emission activity during deforma-tion of rock samples with inhomogeneous fault under biaxial compression

The spatial and temporal distributions of acoustic emission （AE） during the deformation of samples containing an inhomogeneous fault have been studied under biaxial compression. The results show that the fault strength and the duration from loading to failure increase and the failure mode changes from abrupt instability to gradual failure with increase of lateral stress σ2. The pre-setting fault and its heterogeneity play an important role in controlling AE spatial distribution during the deformation. The basic pattern of AE spatial distribution is controlled by the pre-setting fault, especially the parts with inhomogeneous strength and the strong segment, and the localization of fracturing starts from the positions with inhomogeneous strength, With increase of σ5, the dense AE distribution area spreads from the positions with inhomogeneous strength to the whole strong segment gradually. AE temporal sequence is significantly affected by σ2. The fault shows abrupt instability in final failure, which occurs on a background of ＂enhancement-quiescence＂ in microfracturing activity at lower σ2. At higher σ5, the fault shows gradual failure, and AE occurrence rate increases continuously and AER increase exponentially before and after the failure, The effect of σ2 on b-value is also remarkable, b-value shows precursory decrease in the weakening stage when fault behavior is abrupt instability, but it shows balanced change when fault behavior is gradual failure,

A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.

基于MCS-SBL算法的配电网故障定位方法

配电网拓扑结构复杂,传统方法往往需要大量测点信息且难以实现快速有效的故障定位,本文提出基于少量测点信息的故障定位方法。首先,利用等效原理建立一个欠定的故障节点电压方程;其次,利用多重测量向量模型的贝叶斯压缩感知算法求解方程,根据重构稀疏电流矩阵的非零元素位置求解故障区域,实现故障定位;最后,在IEEE33节点配电系统上进行仿真实验,结果表明,所提方法仅需要少量测点的故障前后正序电压分量便可有效定位故障,计算速度较快,并且基本不受故障类型、过渡电阻的影响,同时适用于单故障和多重故障的场景,具有较强的抗噪能力。

基于深度卷积测量网络的滚动轴承压缩域故障特征提取方法

压缩感知可有效降低机械状态监测信号的数据存储和传输压力,而现有压缩感知方法在故障诊断的应用中存在压缩效率低下、信号重构过程缓慢等问题。本文利用自编码网络与压缩感知的对应关系,提出了一种基于深度卷积测量网络的滚动轴承压缩域故障特征提取方法。针对无噪声的故障信号样本难以获取的问题,提出一种利用故障机理构建数据集的方法,利用该仿真数据集训练得到的模型适用于不同工况下的实测轴承信号。构造网络层数由所需要的信号压缩率确定、隐含层与原信号的频率呈对应关系的深度卷积去噪自编码网络。截取训练完备的编码子网络(即深度卷积测量网络)代替传统的观测矩阵对滚动轴承振动信号进行压缩测量,实现压缩域的故障特征提取。仿真分析验证了所提数据集构造方法及压缩域特征提取方法的有效性。滚动轴承实验信号分析进一步验证了采用所提方法训练得到的深度卷积测量网络具有很好的泛化性,且能够在压缩率远低于传统压缩感知方法的情况下有效地提取轴承故障特征成分并进行故障诊断。

压缩感知字典迁移重构的小样本轴承故障诊断

针对实际应用中智能轴承故障诊断面临标签样本严重不足的问题,提出一种结合压缩感知、字典学习和迁移的数据增强算法,用于小样本故障诊断研究。首先,利用源域标签数据通过小波包字典学习和优化方法生成特定源域字典,并得到共享表示系数,获取故障内在信息;之后采用少量目标域信号微调共享表示系数,并更新源域字典生成迁移字典;最后通过共享表示系数和迁移字典生成大量具有目标域特征的新样本,实现数据增强。采用常用的深度故障诊断网络对该数据增强算法进行了诊断性能验证,结果表明该方法产生的信号具有故障的有效信息,用于模型训练和识别能够取得较好的诊断性能。该方法为小样本故障诊断问题提出了新的思路。

基于自适应窗口与压缩幅值的瞬时转频估计

针对变转速工况下轴承信号的时频分布能量发散、噪声和谐波干扰强导致其转频难以获取的问题,提出一种基于自适应窗口和压缩幅值的瞬时转频估计方法。首先,通过自适应窗口在频率轴方向搜索出脊线窗口避免噪声和谐波的干扰;其次,在脊线窗口内用压缩幅值方法集中发散的脊线能量;然后,用惩罚函数法提取脊线,实现转频的精确估计;最后,根据采用轴承实验台收集的数据验证了所提出方法的有效性和鲁棒性。结果表明,相比于传统方法,采用所提方法估计瞬时转动频率使误差降低约8%。

基于日志副本的Raft共识算法优化

在基于三副本策略的分布式存储系统中,当存储节点上的硬盘出现故障时,常见的处理方式是等待系统预设的时间.如果该故障硬盘超时未恢复,才开始恢复故障硬盘上的副本.这种处理方式存在的问题是,当三副本组中存在故障副本时,如果该副本组再有一个副本所在的硬盘发生故障,将导致系统无法继续提供服务,且不能自动恢复.本文提出一种基于日志副本的改进的Raft共识算法,即LR-Raft (log replica based Raft),日志副本没有完整状态机,可以快速加入集群,并参与投票与共识,提升了存在故障硬盘时系统的可用性;可以解决短时间内三副本中两个副本故障导致集群不可用和丢失数据的问题.实验结果表明,在副本组中引入日志副本后,与原Raft相比,LR-Raft在不同的工作负载下读写时延均明显降低,吞吐量显著提升.

基于连续小波变换和模型无关元学习的压燃式活塞发动机气门故障诊断研究

针对压燃式活塞发动机缸盖表面振动信号样本少及传统故障诊断方法特征提取和选择困难的问题,提出了一种基于连续小波变换(continuous wavelet transform,CWT)和模型无关元学习(model agnostic meta learning,MAML)的压燃式活塞发动机气门间隙异常故障诊断方法。通过将CWT的特征提取能力和MAML的快速学习能力相结合搭建故障诊断模型。试验结果表明该方法能有效识别气门间隙故障,并且其准确率高于传统基于CWT和卷积神经网络(convolutional neural network,CNN)的故障诊断方法。通过跨域故障对比试验,研究了不同气门故障类型对模型诊断能力的影响,验证了该方法在解决小样本和跨域故障问题时具有更高的准确率和泛化能力。

辽河坳陷陈家断裂带北部构造演化解析及油气成藏

辽河坳陷陈家断裂带油气资源丰富,是辽河西部凹陷北段重要的含油气区带之一。基于构造演化解析,结合砂箱物理模拟实验,分析了辽河坳陷陈家断裂带的变形机制和演化过程,并指出其对油气成藏的控制作用。研究结果表明:(1)辽河坳陷陈家断裂带具有分段发育特征,且活动时期具有“先北后南”的演化特征,受断层分段性影响,陈家断裂带主要发育压扭背冲构造样式和走滑压扭构造样式。(2)物理模拟实验结果显示,台安—大洼断裂上盘在压扭作用影响下首先形成压扭性质的雁行小断裂,随着位移增大,小断裂逐渐连接,最终形成了贯穿型走滑断裂带,即陈家断裂带。(3)台安洼陷为新生代多期次构造变形叠加形成的洼陷,深层发育主力烃源岩层系,具备发育大规模油气藏的物质基础,洼陷边缘的钻井岩心分析结果显示,烃源岩TOC值为4.79%,Ro为0.3%~0.4%,属于好—较好烃源岩,推测洼陷中心厚度更大,质量更优。陈家断裂带中北部下盘发育断鼻构造,具备陈家洼陷和台安洼陷双源供烃的特点,是油气聚集的有利场所。

A Gel-Based Solidification Technology for Large Fracture Plugging

Fault fractures usually have large openings and considerable extension. Accordingly, cross-linked gel materials aregenerally considered more suitable plugging agents than water-based gels because the latter often undergo contaminationvia formation water, which prevents them from being effective over long times. Hence, in this study, aset of oil-based composite gels based on waste grease and epoxy resin has been developed. These materials havebeen observed to possess high compressive strength and resistance to the aforementioned contamination, therebyleading to notable increase in plugging success rate. The compressive strength, thickening time, and resistance toformation water pollution of these gels have been evaluated indoors. The results show that the compressivestrength of the gel can reach 11 MPa;additionally, the related gelation time can be controlled to be more than3 h, thereby providing a safe construction time;Invasion of formation water has a small effect on the gel strengthand does not shorten the thickening time. All considered performance indicators of the oil-based gel confirm itssuitability as a plugging agent for fault fractures.

三峡升船机齿条模型试验及状态监测方案研究

三峡升船机采用齿轮齿条爬升式垂直升船机型式,其中齿条作为升船机船厢升降运行的传动设备,其设备安全可靠性对升船机的稳定运行至关重要,为其设置智能状态监测系统十分必要。通过小比例试验台,对齿轮有可能出现的缺陷与不良工况进行模拟试验,并采用基于同步压缩小波变换时频谱切片的分析方法及门控循环神经网络的故障诊断方法,进行齿条不良润滑、齿面点蚀与齿根裂纹故障信号的采集与识别。以试验结果为依据,提出了三峡升船机齿条状态监测和故障诊断方案,为三峡升船机齿条状态监测和故障诊断方案的现场实施奠定了良好基础。

基于有限个μPMU和压缩感知理论的主动配电网复故障定位方法

主动配电网(Active distribution network,ADN)动态行为相较传统电网更为复杂,亟需解决其故障检测和定位问题。针对配电网系统中微型同步相量测量装置(Micro phasor measurement unit,μPMU)的运用,提出基于有限个μPMU的复故障定位策略。首先,根据任意线路故障下全网可观的μPMU优化配置方案建立短路、断线故障线路等效模型,通过分析模型将电流故障分量等效至相邻两端节点形成虚拟注入电流向量。其次,利用压缩感知理论求解故障网络节点电压方程,根据重构电流向量中非零元素的位置得到可疑故障节点,并基于可疑故障节点及μPMU配置位置划分动态候选故障域。最后,先分别构建短路、断线区域判据,确定故障区间,再进一步根据区域两端推算电压构造线路判据,精确判断故障线路及类型。基于改进的IEEE 33节点配电网系统进行仿真分析,仿真结果表明,基于有限个μPMU的复故障定位策略是可行的,该策略能够有效判断故障的精确位置。

断控型碳酸盐岩油藏动态储量计算方法

断控型碳酸盐岩油藏非均质性强,不同尺寸的孔、缝、洞交错发育,采用一般物质平衡方法计算的动态储量可能大于静态储量。在引入水油比的基础上,考虑孔缝洞介质不同时的岩石压缩系数,得到了适用于断控型油藏的综合压缩系数,并在此基础上建立了断溶体流动物质平衡新方程,通过数值模拟,验证了新方程的准确性与适用性。结果表明:新方程计算的动态储量与数值模拟得到的静态储量误差仅为0.1099%,验证了新方程准确可靠;哈拉哈塘地区多口井用新方程计算的动态储量与地质建模雕刻的静态储量相对误差为-4.82%~-0.15%,远远低于物质平衡方程计算的动态储量与静态储量的相对误差,新方程计算结果更接近实际情况,更适用于哈拉哈塘地区断控型碳酸盐岩油藏的储量计算。

一种WTMSST结合自适应参数VMD的滚动轴承故障诊断

针对现有轴承故障诊断中常用的时频分析方法存在变换系数在时频平面上分布相对离散,时频谱能量模糊等共性问题,提出了一种基于小波变换的时间重分配多同步压缩变换(WTMSST)结合经蜣螂算法(DBO)优化变分模态分解(VMD)的滚动轴承故障诊断方法。该方法采用了一种根据重分配同步压缩变换(WTSST)优化的WTMSST算法,通过固定点迭代减少了在强频率变化下的群延迟,然后通过以最小包络熵为适应度的DBO算法优化VMD输入参数,根据峭度重构信号后,运用WTMSST进行故障特征提取。采用凯斯西储大学的数据集进行测试,验证该法准确描述了信号的冲击特征,并证明其较过往处理方式具有更好的性能。

基于元迁移学习的压燃式活塞发动机气门故障诊断研究

针对压燃式活塞发动机气门间隙故障振动信号样本少以及跨工况故障诊断困难的问题,提出一种基于元学习和迁移学习的压燃式活塞发动机气门间隙异常故障诊断方法。元学习采用MAML作为学习器,对目标域的支撑集进行数据扩展,提升其泛化能力;迁移学习采用ResNet34作为特征提取网络,并通过SSL替代SL损失函数,压缩源域特征向量之间的距离,为目标域任务提供更多的特征嵌入空间,提升其跨域诊断能力。将预训练和微调后的元学习和迁移学习模型进行决策融合后作为诊断结果输出,并使用发动机台架进行实验数据验证。结果表明,所提方法能在小样本情况下有效识别跨工况气门间隙故障,且效果明显优于单独使用元学习或迁移学习的诊断方法。

煤矿智能压风系统的应用研究

针对传统压风系统多电机之间无法协同配合,压风机房需要定时巡检等问题,设计了一种智能压风系统。该系统通过在控制侧加入可编程控制柜,并引入模糊控制算法,实现压风系统的按压供风、负载均衡、自动轮换的功能。通过布置故障诊断装置,检测压风机的健康状况,在发生故障时可停止压风机的运行,并及时开启另一台压风机,保障管道压力恒定。通过安装视频分析摄像仪实现对压风机房进入人员的监测,分析是否有违规或越界行为,保证人员和设备的安全。智能压风系统的管控平台展示压风机的各项数据,并通过动画生动的展示压风机房内各设备的运行状态,显示压风机健康状况的分析结果。该系统的应用减少了电能的浪费,提供了准确的诊断结果,提高了煤矿智能化水平。