西安交通大学机械工程学院,西安,710049
网络首发:2018-06-10,
纸质出版:2018
移动端阅览
孟文俊 1, 张四聪 1, 淡紫嫣 1, 等. 按类统计方法在滚动轴承可视化可靠性动态评估中的应用[J]. 西安交通大学学报, 2018,52(6):23-29.
Dynamic Assessment of Rolling Bearing Reliability by State Visualization Based on Class Statistics[J]. 2018, 52(6): 23-29.
孟文俊 1, 张四聪 1, 淡紫嫣 1, 等. 按类统计方法在滚动轴承可视化可靠性动态评估中的应用[J]. 西安交通大学学报, 2018,52(6):23-29. DOI: 10.7652/xjtuxb201806004.
Dynamic Assessment of Rolling Bearing Reliability by State Visualization Based on Class Statistics[J]. 2018, 52(6): 23-29. DOI: 10.7652/xjtuxb201806004.
针对目前滚动轴承可靠性评估中性能指标的概率模型大多为单性能指标的静态概率模型
且与实际概率模型误差较大的问题
提出了基于按类统计的滚动轴承可视化可靠性动态评估技术。利用轴承可靠性下降时
其空间状态发生跃迁、概率模型类别会逐渐增多的特点
以滚动轴承的均方根和峭度这两个性能指标为分析对象
采用核密度法建立起初始按类概率模型。对初始按类概率模型进行可视化处理得到初始按类概率图像模型
计算非正常类的图像分布区域面积占总图像分布区域面积的比值得到故障率
进而得到可靠性指标。当数据样本不断累积更新
可实现滚动轴承可靠性的动态评估。对美国智能维护系统中心提供的滚动轴承寿命实验数据进行分析
结果表明
提出的动态按类概率模型能够及时跟踪滚动轴承的退化过程
可靠性指标能够实时反映滚动轴承的退化程度
具有较强的工程实用价值。
Most of the probability models of performance indexes are static probability models of single performance index in the study of the reliability evaluation of rolling bearings
which have large errors compared with the actual probability models. Therefore
a visualized dynamic assessment technology of rolling bearing reliability based on class statistics is proposed. When the bearing reliability declines
there may be a transition in its spatial state
and the probability model category will increases gradually. Selecting the root mean square and kurtosis value of rolling bearings as the analysis object
and the initial class probability model is established by using the nuclear density method. Then the initial class probability model is visualized to obtain the initial class probability image model. The failure rate is the ratio of the abnormal area to the total image area
and the reliability index is obtained. As the data are continuously updated
a dynamic reliability evaluation of the rolling bearings can be achieved. The rolling bearing life test data provided by the American intelligent maintenance system center are analyzed
and the results show that the new dynamic probability model can track the degradation process of rolling bearings in time
so as to reflect the degradation degree of the rolling bearings.
屈梁生, 何正嘉. 机械故障诊断学 [M]. 上海: 上海科学技术出版社, 1986: 4-11.
黄文虎, 夏松波, 刘瑞岩, 等. 设备故障诊断原理、技术及应用 [M]. 北京: 科学出版社, 1996: 9-15.
徐敏, 黄邵毅. 设备故障诊断手册-机械设备状态检测和故障诊断 [M]. 西安: 西安交通大学出版社, 1998: 1-5.
李海波. 智能化轴承故障诊断仪的工程设计与研制 [D]. 沈阳: 沈阳理工大学, 2009: 1-3.
LU H, KOLARIK W J, LU S S. Real-time performance reliability prediction [J]. IEEE Transactions on Reliability, 2001, 50(4): 353-357.
丁锋, 何正嘉, 訾艳阳. 基于设备状态振动特征的比例故障率模型可靠性评估 [J]. 机械工程学报, 2009, 45(12): 89-95.
DING Feng, HE Zhengjia, ZI Yanyang. Reliability assessment based on equipment condition vibration feature using proportional hazards model [J]. Journal of Mechanical Engineering, 2009, 45(12): 89-95.
NELSON W. Analysis of performance degradation data from accelerated tests [J]. IEEE Transactions on Reliability, 2006, 91: 200-208.
马家驹, 梁文梅. 滚动轴承振动统计特性分析 [J]. 轴承, 1994(1): 33-37.
MA Jiaju, LIANG Wenmei. Analysis of vibration statistical characteristics of rolling bearings [J]. Bearing, 1994(1): 33-37.
KALLAPPA P, BYINGTON C S, KALGREN P W, et al. High frequency incipient fault detection for engine bearing components [C]∥Proceedings of the ASME Turbo Expo. New York, USA: ASME, 2005: 413-427.
BYINGTON C S, ORSAGH R, KALLAPPA P, et al. Recent case studies in bearing fault detection and prognosis [C]∥IEEE Aerospace Conference Proceedings. Piscataway, NJ, USA: IEEE, 2006: 1656077.
徐东, 徐永成, 陈循, 等. 基于EMD的灰色模型的疲劳剩余寿命预测方法研究 [J]. 振动工程学报, 2011, 24(1): 104-110.
XU Dong, XU Yongcheng, CHEN Xun, et al. Residual fatigue life prediction based on grey model and EMD [J]. Journal of Vibration Engineering, 2011, 24(1): 104-110.
PARZEN E. On estimation of a probability density function and mode [J]. The Annals of Mathematical Statistics, 1962, 33: 1065-1076.
CAIN J B. An improved probability neural networks and its performance relative to other model [C]∥Application of Artificial Neural Networks. Orlando, USA: SPIE Press, 1990: 358-359.
FUGATE M L, SOHN H, FARRAR C R. Vibration-based damage detection using statistical process control [J]. Mechanical Systems and Signal Processing, 2001, 15(4): 707-721.
符祥, 郭宝龙. 图像插值技术综述 [J]. 计算机工程与设计, 2009(1): 141-144.
FU Xiang, GUO Baolong. Overview of image interpolation technology [J]. Computer Engineering and Design, 2009(1): 141-144.
SAWALHI N, RANDALL R B, ENDO H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis [J]. Mechanical Systems Signal Processing, 2007, 21(6): 2616-2633.
陈保家,汪新波,严文超,等.采用品质因子优化和子带重构的共振稀疏分解滚动轴承故障诊断方法.2018,52(4):70-76.[doi:10.7652/xjtuxb201804010]
夏平,徐华,马再超,等.采用改进HVD与Lempel-Ziv复杂性测度的滚动轴承早期损伤程度评估方法.2017,51(6):8-13.[doi:10.7652/xjtuxb201706002]
张俊红,马梁,鲁鑫,等.机动飞行下挤压油膜阻尼器对碰摩故障转子系统的影响.2015,49(11):62-70.[doi:10.7652/xjtuxb201511011]
李军宁,陈渭,谢友柏.采用知识流理论的高速滚动轴承打滑失效试验台集成设计.2015,49(5):87-93.[doi:10.7652/xjtuxb201505014]
唐贵基,王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用.2015,49(5):73-81.[doi:10.7652/xjtuxb201505012]
易均,刘恒,刘意,等.歪斜安装对组配轴承转子系统动力学特性影响.2014,48(9):107-111.[doi:10.7652/xjtuxb201409 018]
付新哲,张优云,朱永生.滚动轴承故障诊断的案例推理方法.2011,45(11):79-84.[doi:10.7652/xjtuxb201111015]
栗茂林,王孙安,梁霖.利用非线性流形学习的轴承早期故障特征提取方法.2010,44(5):45-49.[doi:10.7652/xjtuxb 201005010]
王晓冬,何正嘉,訾艳阳.滚动轴承故障诊断的多小波谱峭度方法.2010,44(3):77-81.[doi:10.7652/xjtuxb201003016]
0
浏览量
5
下载量
2
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621