融合无量纲指标与信息熵的不同转速下旋转机械故障诊断

摘要
图/表
参考文献(19)
相关文章 (15)

全文: PDF (2100 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对不同转速下旋转机械故障的特征同尺度表征与诊断问题，提出了融合无量纲指标与信息熵的旋转机械故障诊断方法。无量纲指标、信息熵等值与振动能量无关，取决于信号的分散程度与组分比率，对转速敏感性低，故利用无量纲指标与信息熵构建故障特征集，实现不同转速工况下故障特征同尺度定量表征。设计出基于核函数概率估计的故障敏感性指标算法，从所建立的故障特征集中选择对故障敏感性好的特征量构成表征能力更强的故障敏感特征集，并采用线性局部切空间排列(LLTSA)对其进行非线性降维与融合，获得分类特性好、受转速影响小的低维故障敏感特征集。最后，应用鲁棒性好的加权最近邻分类器（WKNNC)实现不同故障类型的诊断。对不同转速下齿轮箱故障进行诊断，结果证明了所提方法的可行性和有效性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	陈仁祥1
	2
	吴昊年1
	韩彦峰2
	赵玲1
	吴志元1
	陈里里1

关键词 ：不同转速, 无量纲指标, 信息熵, 故障诊断

Abstract：Aiming at characterization at the same scale and diagnosis problems for rotating machinery fault features under different rotating speeds, a rotating machinery fault diagnosis method under different rotating speeds based on fusion of non-dimensional index and information entropy was proposed. It was shown that non-dimensional index and information entropy are not related to vibration energy, and they depend on vibration signal’s dispersion level to component ratio, they are less sensitive to rotating speed, so non-dimensional index and information entropy are used to construct fault feature set, and realize fault features’ quantitative characterization at the same scale under different rotating speeds. The calculation method for fault sensitivity index was designed based on core function probability estimation to select features with better sensitivity to faults from the constructed fault feature set, and build a fault sensitive feature set with stronger characterization ability. The linear local tangent space arrangement (LLTSA) was adopted to do nonlinear dimensional reduction and fusion for the fault sensitive feature set. Finally, different fault types were recognized using the weighted K-nearest neighbor classifier (WKNNC) with good robustness. This method was applied to diagnose gearbox faults under different rotating speeds. The results verified the feasibility and validity of the proposed method.

Key words： different speed non-dimensional index information entropy fault diagnosis

收稿日期: 2017-10-18 出版日期: 2019-05-28

引用本文:

陈仁祥1,2，吴昊年1，韩彦峰2，赵玲1，吴志元1，陈里里1. 融合无量纲指标与信息熵的不同转速下旋转机械故障诊断[J]. 振动与冲击, 2019, 38(11): 219-227.
CHEN Renxiang1,2, WU Haonian1, HAN Yanfeng2, ZHAO Ling1, WU Zhiyuan1, CHEN Lili1. Rotating machinery fault diagnosis under different rotating speeds based on fusion of non-dimensional index and information entropy. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(11): 219-227.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I11/219

[1] Barszcz T, Randall R B. Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine. Mech SystSignal Process, 2009, 23: 1352–1365.
[2] 林京,赵明.变转速下机械设备动态信号分析方法的回顾与展望[J].中国科学,2015,45(7):669-686.
LIN Jing, ZHAO Ming. Dynamic signal analysis for speed-varying machinery: A review[J].Sci Sin Tech,2015, 45(7):669-686.
[3] 任凌志,于德介,彭富强,等. 基于多尺度线调频基稀疏信号分解的齿轮故障信号阶比分析[J].机械工程学报,2011,47(13): 92-97.
REN Lingzhi,YU Dejie,PENG Fuqiang,etal.Order Analysis of Gear Fault Signals Based on Multi-scale Chirplet and Sparse Signal Decomposition[J].Journal Of Mechanical Engineering, 2011, 47(13): 92-97.
[4] 彭富强,于德介,武春燕.基于自适应时变滤波阶比跟踪的齿轮箱故障诊断[J]. 机械工程学报, 2012,48(7):77-85.
PENG Fuqiang, YU Dejie, WU Chunyang. Self-adaptively Time-varying Filter Based Order Tracking Method and Its Application in Gearbox Fault Diagnosis[J].Journal Of Mechanical Engineering, 2012, 48(7):77-85.
[5] Zhao M, Lin J, Xu X, et al. Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds[J]. Sensors, 2013, 13: 10856–10875.
[6] Eggers B L, Heyns P S, Stander C J. Using computed order tracking to detect gear condition aboard a dragline[J]. J S Afr I Min Metall, 2007,107: 115.
[7] Heyns P S, Stander C J, Heyns T, et al. Vibration based condition monitoring under fluctuating load and speed conditions[C]. 18th World Conference on Nondestructive testing, 16-20 April 2012, Durban, South Africa.
[8] Zhao M, Lin J, Xu X, et al. Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis[J]. Sensors, 2014, 14: 20320–20346.
[9] Li C, Liang M. A generalized synchro squeezing transform for enhancing signal time-frequency representation[J]. Signal Process, 2012, 92:2264–2274
[10] Chaari F, Abbes M, Rueda F, et al. Analysis of planetary gear transmission in non-stationary operations[J]. Front Mech Eng, 2013, 8: 88–94
[11] Vicuña C M. Effects of operating conditions on the Acoustic Emissions (AE) from planetary gearboxes[J]. Appl Acoust, 2014, 77: 150–158.
[12] Feng Li a,Minking K. Chyu,Jiaxu Wang,etal.Life grade recognition of rotating machinery based on Supervised Orthogonal Linear Local Tangent Space Alignment and Optimal Supervised Fuzzy C-Means Clustering[J]. Measurement,2015, 73: 384–400.
[13] Liu X M，Yin J W，Feng Z L，etal．Orthogonal neighborhood preserving embedding for face recognition[C].2007 IEEE International Conference on Image Processing， ICIP 2007．New York，USA: IEEE，2007: 133-136．
[14] Li Feng, Tang Baoping, Yang Rongsong. Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment[J], Measurement,2013,46(8): 2525-2539.
[15] 岑健,张清华,胥布工,等. 无量纲免疫检测器在缓变故障检测中的应用[J]. 华南理工大学报: 自然科学版, 2009, 37( 5) : 64-67.
Cen Jian, Zhang Qinghua, Xu Bugong,etal. Application of Nondimensional immune detector to slowly varying fault detection[J]. Journal of South China University of Technology, 2009, 37( 5) : 64-67.
[16] 张清华,邵龙秋,李红芳,等.基于无量纲指标的旋转机械并发故障诊断技术[J].华中科技大学学报: 自然科学版,2009,37:156-159.
Zhang Qinghua, Shao Longqiu, Li Hong fang, etal. Research of concurrent faults diagnosis technology of rotating machinery based on the non-dimensional parameter[J].J .Huazhong Univ .o f Sci .& Tech.(Natural Science Editio n), 2009,37:156-159.
[17] Lei Yaguo, Zuo MingJ. Gear crack level identification based on weighted K nearest neighbor classification algorithm[J]. Mechanical Systems and Signal Processing, 2009,23(5):1535-1547.
[18] Chen Lifei, Guo Gongde. Nearest neighbor classification of categorical data by attributes weighting[J]. Expert Systems with Applications, 2015,42 (6): 3142–3149.
[19] Roberto Souza, Letícia Rittner , Roberto Lotufo. A comparison between k-Optimum Path Forest and k-Nearest Neighbors supervised classifiers[J]. Pattern Recognition Letters, 2014,39:2–10.