Rotating machinery fault diagnosis under different rotating speeds based on fusion of non-dimensional index and information entropy

PDF(2100 KB)

Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (11) : 219-227.

CHEN Renxiang1,2, WU Haonian1, HAN Yanfeng2, ZHAO Ling1, WU Zhiyuan1, CHEN Lili1

Author information +

History +

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

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

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[J]. Journal of Vibration and Shock, 2019, 38(11): 219-227

References

[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.