借鉴日本成功经验发展我国轴承钢生产技术

介绍日本发展轴承钢生产技术的有益经验，认为提高我国高碳铬轴承钢质量的根本途径是采用新技术，即发展推广带偏心底出钢的高功率电炉、普及炉外精炼技术、发展连铸技术等，建议发展EF＋LF＋CC的轴承钢生产流程。     

基于机器视觉的轴承尺寸检测

本文采用机器视觉和图象处理技术，利用CCD摄象机、图象采集卡、运动控制卡、计算机等组成一针对轴承外形尺寸的视觉检测系统，介绍了系统的组成和图象处理方法，并给出了系统的硬件和软件设计。     

稳定冷轧轴承钢带球化退火工艺

对冷轧真空精炼轴承钢带的球化退火工艺进行了试验，并探讨了冷轧和热处理工艺对球化的影响，得出了较为理想的球化退火工艺和退火后的性能，并取得了较好的经济效益。     

基于Taguchi方法的滑动轴承二道次旋压工艺参数优化

以QSn7-0.2锡青铜为试验材料,以减薄率、道次分配比、一道次进给比和二道次进给比为试验参数,设计正交试验,以成形件内外轴线直线度误差为评价指标,利用Simufact软件对二道次错距旋压进行数值模拟,利用Taguchi方法分析仿真结果的信噪比,优化试验参数,得到各参数对滑动轴承内外轴线直线度误差的影响程度顺序与最优工艺参数组合,并进行试验验证。结果表明,Taguchi方法可有效降低工件内外轴线直线度误差,提高产品性能。     

轴承球的声发射检测技术

相同条件下运动的轴承球下落到装有换能器的反弹块上，换能器将接收的信号转换成电信号，经过放大和滤波等形成可记录的声发射信号。实验表明，根据单位时间的振铃计数可将缺陷球识别出来。此方法具有速度快、可靠性高、易于实现自动化等优点。     

控制力矩陀螺高速转子轴承保持架仿真分析

建立了控制力矩陀螺高速转子球轴承保持架动力学仿真分析模型,并以B7005C球轴承为例,分析了不同引导间隙、兜孔间隙对保持架质心轨迹、保持架摩擦力矩的影响,据此进行了保持架间隙的优化,得出了适用于该工况的最优保持架间隙。     

京南水电站机组轴承冷却系统降低油温的技术改造

分析了京南水电站机组轴承冷却系统在运行中出现冷却水温度过高的原因,提出了以河水代替循环冷却水的技术改造方案,并论述了改造方法和效果.     

轴承故障音频诊断系统设计与开发

轴承音频信号包含了其运行状态的许多重要信息,通过这些信息的恰当分析、处理就能对轴承故障进行有效诊断.与振动信号相比,音频信号的采集是非接触式的,具有使用方便、成本低廉等优势.因此,将基于音频信号的隐马尔可夫模型(Hidden Markov Model,HMM)建模方法引入到轴承故障诊断研究,以PC机为平台,在Visual C 7.0环境下设计开发了一套功能完善、操作方便、界面友好的轴承音频故障诊断系统.主要介绍了诊断系统的体系结构,HMM方法建模的步骤和故障诊断过程中的具体应用操作.多次实验结果表明,效果良好.     

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified.     

高频谱峰群在轴承故障诊断中的意义

论证了轴承信号中高频谱峰群的存在,指出了高频谱峰群中含有轴承元件疲劳剥落缺陷的重要信息,对轴承的故障诊断具有重要意义．