椭圆弧齿线圆柱齿轮激光非接触测量方法

针对目前椭圆弧齿线圆柱齿轮测量方法的缺乏，设计了一种基于激光位移传感器的椭圆弧齿线圆柱齿轮精密测量装置。阐述了椭圆弧齿线圆柱齿轮的几何形状特征，介绍了激光非接触式测量装置的构成与测量原理，通过对该齿轮多个径向截面轮廓的激光测量，建立齿轮测量截面的轮廓数据模型；对截面轮廓数据模型进行坐标转换，计算出齿轮中间截面的齿距偏差；分析同一轮齿不同截面的齿廓测量数据，计算不同截面分度圆与齿廓交点坐标值，计算出椭圆弧齿线的整体偏差值。该方法能够有效填补曲线齿线圆柱齿轮的测量技术空白，且具有较高的测量精度和效率；其同样也适用于其他圆柱齿轮测量

椭圆弧齿线圆柱齿轮加工方法分析与加工仿真

在分析现有圆弧齿线圆柱齿轮基础上，阐述了一种渐开线椭圆弧齿线圆柱齿轮的主要几何参数：齿廓为渐开线齿廓，任意垂直轴线的截面上周向齿厚、周向齿槽宽、分度圆处的压力角相等，齿线在分度圆柱面上的展开线是对称椭圆弧。推导了齿轮齿面方程、端面齿廓方程和接触线方程。提出了倾斜式旋转刀盘齿轮加工方法，利用VERICUT建立齿轮专用加工刀具模型，对椭圆弧齿线圆柱齿轮进行仿真加工，利用仿真后得到的齿轮模型进行了齿轮副接触线特性分析。结果表明，椭圆弧齿线圆柱齿轮副可以实现全齿宽线接触，验证了加工方法的可行性，为渐开线椭圆弧齿圆柱齿轮的加工和应用提供基础

离散式变径带轮无级变速器设计与分析

提出一种新型面接触式摩擦传动结构的无级变速器——离散式变径带轮无级变速器。分析了离散式变径带轮传动结构的尺寸参数与传动比的关系，推导出离散式变径带轮传动临界摩擦力的计算公式，验证了离散式变径带轮理论上具有大转矩传动能力。另外，进行了基于ADAMS的转速波动性分析，并搭建离散式变径带轮无级变速器实验装置进行测试验证，结果表明，其转速波动满足大部分设备的传动要求。新型离散式变径带轮无级变速器结构紧凑、设计合理，具有很大的开发价值

椭圆弧齿圆柱齿轮的一种铣削加工方法

在研究圆弧齿线圆柱齿轮的基础上,提出了全齿宽啮合型椭圆弧齿圆柱齿轮的铣削加工方法。通过分析其理想几何参数,推导了齿轮齿面及啮合重合度方程。利用齿轮齿条模型分析了刀具与椭圆齿线的形成过程,提出椭圆弧齿圆柱齿轮的铣削加工原理,并进行了齿轮的铣削加工。斜安装的刀具加工形成的齿轮在径向截面上都是渐开线齿廓,齿线在分度圆柱面的展开曲线为椭圆的一部分,凸齿面齿线与凹齿面齿线具有相同的几何形状参数。该加工原理可以完成椭圆弧齿圆柱齿轮的高效铣削,也适用于该齿轮的磨削

One-dimensional multi-scale domain adaptive network for bearing-fault diagnosis under varying working conditions

Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1D-MSDAN is a kind of deep transfer model, which uses both feature adaptation and classifier adaptation to guide the multi-scale convolutional neural network to perform bearing-fault diagnosis under varying working conditions. Feature adaptation is performed by both multi-scale feature adaptation and multi-level feature adaptation, which helps in finding domain-invariant features by minimizing the distribution discrepancy between different working conditions by using the Multi-kernel Maximum Mean Discrepancy (MK-MMD). Furthermore, classifier adaptation is performed by entropy minimization in the target domain to bridge the source classifier and target classifier to further eliminate domain discrepancy. The Case Western Reserve University (CWRU) bearing database is used to validate the proposed 1D-MSDAN. The experimental results show that the diagnostic accuracy for the 12 transfer tasks performed by 1D-MSDAN was superior to that of the mainstream transfer learning models for bearing-fault diagnosis under variable working conditions. In addition, the transfer learning performance of 1D-MSDAN for multi-target domain adaptation and real industrial scenarios was also verified.</p

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies