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1. 江苏科技大学 机械工程学院, 江苏 镇江 212000
2. 火箭军工程大学 控制工程系, 陕西 西安 710025
3. 武汉滨湖电子有限责任公司, 湖北 武汉 430205
Received:15 August 2023,
Published Online:30 October 2024,
Published:31 October 2024
移动端阅览
Honggen ZHOU, Xiaodie REN, Li SUN, et al. Study of JUST Slewing Bearing Failure Test Data[J]. Acta Armamentarii, 2024, 45(10): 3744-3753.
Honggen ZHOU, Xiaodie REN, Li SUN, et al. Study of JUST Slewing Bearing Failure Test Data[J]. Acta Armamentarii, 2024, 45(10): 3744-3753. DOI: 10.12382/bgxb.2023.0756.
回转支承作为军用雷达的关键部件
对保证设备安全使用、提高效益具有重大作用。通过数据驱动方法对回转支承运行状态进行实时监测与诊断
已成为该技术领域的研究热点。然而回转支承面临服役工况复杂、故障试验样本稀少等问题
使其故障诊断技术研究一直饱受数据不足的困扰
也严重制约了军用机械装备寿命预测与健康管理技术的发展与应用。为此开展了回转支承故障试验
通过对回转支承运转过程中的多向振动、声发射信号进行采集
形成了故障试验数据集。该数据集包含回转支承在9种工况下的多向振动信号和声发射信号
且明确标注了回转支承的信号采集时间、故障标签、转速、负载、采集次数等多种相关信息
为基于数据驱动的回转支承故障诊断与雷达伺服系统健康管理提供数据支撑和技术保障。
As a key component of military radar
the slewing bearing plays a significant role in ensuring the safe use of an equipment and improving its efficiency. The real-time monitoring and diagnosis of slewing bearing operation state through data-driven methods have become a hot research topic in this technical field. However
the slewing bearing is faced with the problems of complex service conditions and scarce failure test samples so that its fault diagnosis technology research has been plagued by the lack of data and the development and application of the prediction and health management technology of military machinery and equipment are seriously constrained. For this reason
the slewing bearing failure is tested
and a failure test data set is generated by collecting the multidirectional vibration and acoustic emission signals during the operation process of slewing bearing. The data set contains the vibration signals and acoustic emission signals of slewing bearing under nine working conditions
and the signal acquisition time
fault label
rotational speed
load
number of acquisitions and other related information of slewing bearing are clearly labeled
which provides data support and technical guarantee for the fault diagnosis of data-driven slewing bearing and the health management of radar servo system.
张占立 , 周鹏举 , 李文博 , 等 . YRT转台轴承摩擦力矩特性研究 [J ] . 兵工学报 , 2019 , 40 ( 7 ): 1495 - 1502 . DOI: 10.3969/j.issn.1000-1093.2019.07.020 http://doi.org/10.3969/j.issn.1000-1093.2019.07.020 为研究YRT转台轴承的摩擦力矩特性,在对其静力学分析基础上建立了摩擦力矩模型,并对摩擦力矩模型进行了试验验证。基于模型计算结果,分析了工况参数、轴向游隙和滚子修形对摩擦力矩特性的影响规律。结果表明:YRT转台轴承在承受轴向载荷时,摩擦力矩主要由上排滚子产生,且摩擦力矩曲线上存在一拐点,拐点前后摩擦力矩与轴向载荷基本呈比例关系,但比例系数不同;在低速范围内,摩擦力矩随着转速的增大逐渐增大,但总体变化幅度较小;YRT转台轴承摩擦力矩随轴向游隙的减小逐渐增大,且增幅逐渐增大;使用全凸圆弧修形滚子可以有效地降低摩擦力矩。
ZHANG Z L , ZHOU P J , LI W B , et al . Characterization of friction torque of YRT rotary table bearing [J ] . Acta Armamentarii , 2019 , 40 ( 7 ): 1495 - 1502 . (in Chinese)
陈渐伟 , 于传强 , 刘志浩 , 等 . 多轴特种车辆的数据建模方法及横向动力学应用 [J ] . 兵工学报 , 2023 , 44 ( 1 ): 165 - 175 . DOI: 10.12382/bgxb.2022.0811 http://doi.org/10.12382/bgxb.2022.0811 多轴特种车辆的动力学模型具有强非线性,精细化的物理建模需要准确的模型参数和动力学方程以映射车辆动力学的特性。在无精确的车辆先验物理参数信息和动力学函数关系条件下,为提高车辆动力学建模的保真度,针对某型五轴特种车辆的横向动力学行为,提出了一种基于神经网络的数据建模方法。网络框架主体呈闭环结构,网络输出的状态信息同时作为输入用于预测下一时刻的状态,实现了数据建模递归更新;针对闭环网络模型,设计了闭环结构的训练策略, 在网络模型中引入中间变量,使得网络在训练阶段仍然保持闭环结构;网络模块采用循环门控单元(Gate Recurrent Unit)和全连接网络(Full Neural Networks)的组合方式;数据集由经过实车验证的Trucksim仿真模型生成,分析结果表明:在无精确车辆先验信息条件下,物理建模难以准确预测出车辆的状态信息,数据模型具有更好的保真度。闭环训练方法可以使得闭环结构的网络具有更好的保真度,对于横向速度和横摆角速度预测的最大绝对值误差仅为0.079 km/h和0.342°/s,相比于开环训练的结果,最大误差降低了58.40%和49.48%。
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封杨 , 黄筱调 , 洪荣晶 , 等 . 基于圆域分析的大型回转支承初期故障诊断 [J ] . 振动与冲击 , 2017 , 36 ( 9 ): 108 - 115 .
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曾耀传 , 林云树 , 吴晓梅 . 基于EEMD与GWO-MCKD的门座起重机回转支承故障诊断 [J ] . 机床与液压 , 2022 , 50 ( 7 ): 170 - 175 . DOI: 10.3969/j.issn.1001-3881.2022.07.031 http://doi.org/10.3969/j.issn.1001-3881.2022.07.031 低速重载的门座起重机回转支承信号易受环境噪声影响,难以提取故障特征。为解决此问题,提出一种集合经验模态分解(EEMD)与灰狼优化(GWO)算法优化的最大相关峭度解卷积(MCKD)相结合的故障诊断方法。对回转支承信号进行EEMD分解,得到一系列本征模态函数(IMF),选择峭度最大的IMF作为最优分量;以相关峭度为目标函数,利用GWO寻找在最优分量上的MCKD的最佳参数组合;使用最佳参数组合的MCKD对最优分量进行降噪,突出故障冲击成分;对降噪后的信号进行包络谱分析,完成故障诊断。结果表明:所提方法能自适应增强故障冲击成分,有效提取故障特征。
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郑强 , 林云树 , 吴晓梅 , 等 . 基于VMD与自适应MOMEDA的回转支承故障诊断 [J ] . 组合机床与自动化加工技术 , 2022 ( 5 ): 79 - 82 . DOI: 10.13462/j.cnki.mmtamt.2022.05.019 http://doi.org/10.13462/j.cnki.mmtamt.2022.05.019 针对强背景噪声下的低速重载回转支承难以提取故障特征的问题,提出了一种变分模态分解(VMD)与多点最优最小熵解卷积(MOMEDA)相结合的回转支承故障诊断方法。首先,采用VMD算法对原始振动信号进行分解,从中选出峭度最优分量;其次,利用灰狼优化算法(GWO)优化MOMEDA算法中的参数T,再基于优化的MOMEDA算法增强最优分量中的故障冲击成分;最后,对处理后的最优分量进行包络谱分析,提取故障特征。与VMD-MED方法相比,所提方法能够更准确突出信号中的周期性故障冲击成分,有效提取低速重载回转支承故障特征。
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李云飞 , 苏文胜 . 基于时序灰度图和分组ResNet的回转支承故障诊断 [J ] . 机床与液压 , 2022 , 50 ( 12 ): 187 - 191 . DOI: 10.3969/j.issn.1001-3881.2022.12.034 http://doi.org/10.3969/j.issn.1001-3881.2022.12.034 为解决大型回转支承转速低、背景噪声大、常规的声发射诊断方法难以适用的问题,提出一种基于灰度图和ResNet模型相结合的声发射信号处理方法。将声发射信号编码为二维灰度图像,并通过ResNet模型识别声发射信号编码得到的灰度图,通过训练模型实现对大型回转支承的故障诊断。对某型号大型回转支承进行试验,结果表明:以时序二维化后的灰度图作为故障诊断依据,可以显著提高回转支承的故障诊断准确率;相比于传统方法,所提方法泛化性能和鲁棒性能更好,可以很好地应用在实际工况中的大型回转支承故障诊断。
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