基于低秩稀疏分解算法的航空锥齿轮故障诊断

摘要
图/表
参考文献(20)
相关文章 (15)

全文: PDF (2497 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要锥齿轮是航空发动机传动系统改变传动方向和传递功率的核心部件，常工作于高速、重载的条件下，不可避免发生齿面损伤及疲劳断裂等故障，其故障模式复杂、背景噪声强烈，难以有效诊断。针对上述问题，本文首先研究了旋转机械特征的自相似属性，分析了观测信号中干扰信息和特征信息的奇异值分布差异性，建立了二维特征矩阵的奇异值稀疏低秩先验，并将其建模为稀疏核范数正则描述，进而构建了低秩稀疏分解模型，提出了基于广义块坐标优化理论的模型求解算法框架。最后，将算法用于滑油附件锥齿轮的故障诊断，有效地辨识了潜在的故障，并与经典的稀疏正则和标准的谱峭度算法进行对比分析，结果证实了算法的优越性。

	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

关键词 ：锥齿轮, 航空发动机, 稀疏分解, 低秩, 自相似

Abstract：Bevel Gear is a key component in aero-engine transmission system, which always works in harsh environment such as high speed and high load. Therefore, they inevitably suffer performance degradation. However, the observed signals are also contaminated by strong background noises and harmonic interferences. In the paper，a novel low rank sparse decomposition method is proposed for aero-engine bevel gear fault diagnosis. Firstly, due to the self-similarity of impulsive feature, an adaptive partition window is designed to transform the impulsive feature into a data matrix. By performing the SVD decomposition, the singular value distribution of feature signal exhibits sparse property, and then the sparse low rank prior of feature signal is established, which is further modeled by nuclear norm. Subsequently, by incorporating the classic sparse learning model and the nuclear norm of feature signal, a novel sparse low rank model is proposed. Furthermore, a proximal gradient basedonblock coordinate decent solver is also developed. The effectiveness of the proposed model and algorithm are evaluated through performing the diagnosis of aero-engine bevel gear.

Key words： Bevel Gear； Aero- Engine；Sparse decomposition；Low rank；Self-similarity

收稿日期: 2019-01-21 出版日期: 2020-06-15

引用本文:

陈礼顺1,2，张晗3，陈雪峰4，程礼2，曾林2. 基于低秩稀疏分解算法的航空锥齿轮故障诊断[J]. 振动与冲击, 2020, 39(12): 103-112.
CHEN Lishun1,2，ZHANG Han3，CHEN Xuefeng4，CHENG Li2,5，ZENG Lin2. Fault diagnosis of aero-engine bevel gear based on a low rank sparse model. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(12): 103-112.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I12/103

[1] 郭梅, 陈聪慧, 胡兴海, 等. 航空发动机附件机匣结构设计方法研究 [J]. 机械传动, 2017, 41: 211-216.
GUO M, CHEN C H, HU X H, et al. Research of the Aero Engine Accessory Gearbox Structure Design Method [J]. Journal of Mechanical Transmission, 2017，41：211-216.
[2] 徐萍. 齿轮的磨损失效及修复 [J]. 甘肃冶金, 2010, 32: 112-113.
XU P. Gear Wear Failure and Repair [J].GANSU Metallurgy, 2010,32：112-113.
[3] 赵韩, 吴其林, 黄康,等. 国内齿轮研究现状及问题研究 [J]. 机械工程学报, 2013, 49: 11-20.
ZHAO H，WU Q L, HUANG H, et al. Status and Problem Research on Gear Study[J].Journal of Mechanical Engineering, 2013, 49：11-20.
[4] LEI Y, LIN J, ZUO M J, et al. Condition monitoring and fault diagnosis of planetary gearboxes: A review [J]. Measurement, 2014, 48:292-305.
[5] HE G, DING K, LIN H. Gearbox coupling modulation separation method based on match pursuit and correlation filtering [J]. Mechanical Systems and Signal Processing, 2016, 66-67:597-611.
[6] CHEN J, LI Z, PAN J, et al. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review [J]. Mechanical Systems and Signal Processing, 2016, 70–71:1-35.
[7] WANG Y, XIANG J, MARKERT R, et al. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications [J]. Mechanical Systems and Signal Processing, 2016, 66:679-698.
[8] CHEN S S, DONOHO D L, SAUNDERS M A. Atomic Decomposition by Basis Pursuit [J]. Siam Review, 2001.
[9] FU W, LI S, FANG L, et al. Adaptive Spectral-Spatial Compression of Hyperspectral Image With Sparse Representation [J]. Transactions On Geoscience and Remote Sensing, 2017, 55: 671-682.
[10] OVEIS A H, SEBT M A. Compressed sensing-based ground MTI with clutter rejection scheme for synthetic aperture radar [J]. Iet Signal Processing, 2017, 11(2): 155-164.
[11] 张晗, 杜朝辉, 方作为, 等. 基于稀疏分解理论的航空发动机轴承故障诊断 [J]. 机械工程学报, 2015, 51: 97-105.
ZHANG H, DU C Z, FANG Z W, et al. Sparse Decomposition Based Aero-engine’s Bearing Fault Diagnosis, 2015, 51: 97-10.
[12] DU Z, CHEN X, ZHANG H, et al. Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault Diagnosis [J]. IEEE Transactions on Industrial Electronics, 2015, 62: 6594-6605.
[13] FAN W, CAI G, ZHU Z K, et al. Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction [J]. Mechanical Systems and Signal Processing, 2015, 56-57:230-245.
[14] DU Z, CHEN X, ZHANG H, et al. Weighted low-rank sparse model via nuclear norm minimization for bearing fault detection [J]. Journal of Sound and Vibration, 2017, 400:270-287.
[15] FENG Z, LIANG M. Complex signal analysis for planetary gearbox fault diagnosis via shift invariant dictionary learning [J]. Measurement, 2016, 90:382-395.
[16] ZHANG H, CHEN X, DU Z, et al. Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis [J]. Mechanical Systems and Signal Processing, 2017, 94:499-524.
[17] XU Y, YIN W. A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion [J]. SIAM Journal on Imaging Sciences, 2013, 6: 1758-1789.
[18] REDONT P, SOUBEYRAN A. Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality [J]. Mathematics of Operations Research, 2010, 35: 438-457.
[19] RANDALL R B, ANTONI J R M. Rolling element bearing diagnostics-A tutorial [J]. Mechanical Systems and Signal Processing, 2011, 25: 485-520.
[20] LI Y, LIANG X, XU M, et al. Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform [J]. Mechanical Systems and Signal Processing, 2017, 86, Part A(204-223）.