浅谈汽车机械故障原因及防范措施

伴随现代信息科技水平的不断发展,人们开始追求高质量的物质生活水平,交通运输方式也变得更为多样化,汽车作为快捷的交通方式开始普及发展,然而,伴随汽车销售市场的不断扩大,汽车的使用问题也逐渐暴露出来。因此,本文针对汽车经常发生的机械故障问题进行分析,同时提出有效的防范措施,保障汽车可以适当延长使用期限。     

Group decision support system for backbone-network reconfiguration

Modern power system is unprecedentedly vulnerable and the society is exposed to higher blackout risks. Therefore restoration is a key issue to power systems. Reasonable backbone-network reconfiguration is necessary for re-establishing the network and restoring loads quickly. In order to speed up network reconfiguration under security constraints, a group decision support system (GDSS) is developed. A unified data platform based on data warehouse is constructed to resolve inconsistencies of data, information and models. A three-stage restoration strategy and a two-layer restoration path searching method are proposed to establish restoration schemes. Multiple attribute decision-making is implemented to evaluate alternatives and consider various attributes about restoration speed and system security comprehensively. Group decision-making provides an aggregated cardinal ranking of candidate restoration schemes. The GDSS uses expert rules for qualitative analysis and numerical computation programs for quantitative analysis, and it can resolve the semi-structured decision problem of network reconfiguration efficiently. Restoration plan of Shandong power grid is developed with the proposed method and it has been adopted by State Grid Shandong Electric Power Company. Performance results of Shandong power grid of China demonstrate the feasibility and flexibility of the method.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.     

一种新的风电场复杂下垫面场地校准模型

针对现有方法模型简单、气象因素考虑不足和缺少异常数据检测等问题,提出一种基于相对密度离群因子（relative RDOF）异常检测和支持向量回归（SVR）组合的场地校准模型。首先,通过RDOF与四分位法对测风塔与风电机组距下垫面相同高度风速间的风速比进行异常数据检测,间接实现风速预处理;同时,根据测风塔与风电机组相同高度风切变序列的相关性,动态引入大气稳定度等级,与多种常规自然因素共同作为模型输入。然后,建立SVR场地校准模型修正风速。最后,通过算例验证,并与国际电工委员会（IEC）标准方法进行对比。结果表明,该方法可有效提升场地校准模型的风速修正精度。     

软岩与水相互作用研究综述

软岩是一类松散、破碎、软弱及风化膨胀性的强度较低的岩石,在水环境作用下软岩易产生大变形失稳破坏。大多数岩土工程事故都涉及到软岩与水的相互作用,研究软岩与水相互作用对于分析某些由软岩失稳引起的工程事故具有重要的理论和现实意义。针对软岩与水相互作用这一问题,从软岩与水相互作用的分类、软岩吸水特性的影响因素及其软岩吸水失稳机理等方面,对国内外具有代表性的研究成果进行了梳理。结果表明:软岩与水相互作用可分为力学作用、物理作用和化学作用。在各种影响软岩吸水特性的因素中,主要因素是黏土矿物的含量和种类、孔隙结构,其他影响因素有水压变化、软岩的干湿循环次数、软岩的块体尺度(尺度效应)和吸水时间等。软岩吸水失稳的根本原因是吸水后黏土矿物微观结构发生变化,而是否含蒙脱石等吸水性极强的黏土矿物并非直接原因;后者只是对软岩吸水过程起到促进作用。     

基于增强现实的硬件维护系统研究与设计

传感器、多媒体、机器学习等技术的快速发展,促进了增强现实在多领域得到普及和应用。航空设备硬件运行维护也是一个重要领域,可以基于增强现实构建一个硬件维护系统,渲染一个虚拟的硬件运行环境,模拟实际的故障发生情况,加强对硬件设备的装配和维修,进一步提高航空设备硬件运行维护的水平。本文详细地描述了增强现实在硬件维护系统中的应用功能,分别是虚拟环境仿真功能、装配维护功能、3D模型设计功能,同时构建一个强大的硬件维护系统,该系统沉浸感强、娱乐性足,能够激发硬件维护人员的学习和应用兴趣,具有重要的作用和意义。     

在轨维修后端模块接口机构的研究

在轨维修可以大大的延长空间科学仪器的使用寿命,节省大量的经济成本。为了实现空间望远镜后端模块的在轨操作和更换,设计了一套与之对应的接口机构,能够解决在轨快速定位与安装。根据321运动学定位准则,详细介绍了该接口机构的内部组成和工作原理;然后把该接口机构与后端模块进行组件级有限元仿真,仿真结果表明一阶模态远高于整机基频可以有效地避免发射时候的共振;设计了一套平面内的工装来模拟后端模块,利用等效质量法对整个机构进行重力卸载;搭建实验平台,利用激光跟踪仪来测量整个后端模块的重复定位安装精度,实验数据表明,X,Y,Z 3个方向的重复平移定位误差分别为±5.58μm,±3.24μm及±3.63μm,优于总体指标±10μm;热实验结果表明整个机构可以完全释放由于温度变化产生的形变,具有很高的热稳定性;使入射光线和靶面的相对位置持续稳定,保证了较高的成像质量。为其他空间在轨维护装置提供强有力的参考价值。     

巷道过陷落柱顶板安全技术措施

为了保证9109回风顺槽安全快速过X0陷落柱,保证过陷落柱期间顶板稳定性,七一煤矿通过技术研究,采用导硐分层爆破施工工艺,提出了“撞楔超前支护+注浆锚棚支护”联合加强支护措施。通过实际应用,过陷落柱期间顶板未发生冒落现象,顶板下沉量控制在0.13m以下,取得了显著成效。     

大工作阻力放顶煤液压支架结构改进设计

为解决某矿使用的放顶煤液压支架出现支架压死、后立柱爆缸、结构件开裂等问题,通过地质条件分析,探究适应此类地质条件的液压支架改进方法,在此基础上,将现用液压支架架型由ZF15000/26/42改进为ZF21000/25/45D,并对支架整体结构进行针对性优化,以矿压监测数据为边界条件,对整架进行有限元分析。经样机研制及批量工业性试验,结果表明该支架架型布局及局部结构适应此类地质条件的综放开采,丰富了具备此类地质条件的综放开采液压支架应用案例。