Abstract:Nonlocal means (NLM) method has been widely applied to image processing in recent years and effectively overcomes the limitations of neighborhood filter. NL-Means has become very popular in the field of 2D image signal processing, and then extended to vibration signal processing for fault diagnosis of rolling bearings. NLM is an emerging method to tackle such problem with the ability to eliminate noise. Unfortunately, the nonlocal means is unable to trim down all the noise in the presence of strong interference. Aiming at such a dilemma, a novel fault diagnosis method for rolling element bearings was proposed based on the weight of non-local means (NLM) de-noising. The impact feature is reflected in the form of weight by the weighting operation. Then, envelope analyses are performed on weights to allow for easier detection of such fault characteristic frequencies. Compared with EEMD, the effectiveness and merits of the proposed method were verified by experimental data and a field case.
张 龙, 胡俊锋, 熊国良. 基于加权非局部平均算法的滚动轴承故障诊断[J]. 振动与冲击, 2016, 35(19): 156-161.
ZHANG Long, HU Jun-feng, XIONG Guo-liang . Fault diagnosis of rolling bearing based on the weight of the nonlocal means. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(19): 156-161.
[1] 蔡艳平,李艾华,石林锁,白向峰,沈金伟. 基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J]. 振动与冲击,2011,30(2):167-172+191.
CAI Yan-Ping, LI Ai-hua, SHI Lin-suo, et al. Roller bearing fault detection using improved envelope spectrum analysis based on EMD and spectrum kurtosis[J]. Journal of Vibration and Shock, 2011, 30(2):167-172+191.
[2] 苏文胜,王奉涛,张志新,郭正刚,李宏坤. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]. 振动与冲击,2010,29(3):18-21+201.
SU Wen-sheng, WANG Feng-tao, ZHANG Zhi-xin, et al. Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J]. Journal of Vibration and Shock, 2010, 29(3):18-21+201.
[3] 梁鹏鹏. 滚动轴承故障诊断实验样机的开发[D].北京:北方工业大学,2008.
Liang Peng-peng. Development of Test Prototype of Rolling Bearing Fault Diagnosis[D]. Beijing: North China University of Technology,2008.
[4] MCFADDEN P,SMITH D. Model for the vibration produced by a single point defect in a rolling element bearing[J]. Journal of Sound and Vibration, 1984, 98(2):263-273.
[5] MCFADDEN P, SMITH J. Vibration monitoring of rolling element bearings by the high-frequency resonance technique-a review [J]. Tribology International, 1984, 17(1):3-10.
[6] Buades A, Coil B, Morel J M. A Review of Image Denoising Algorithms with a New One[J], Multiscale Modeling & Simulation, 2005,4(2):490-530.
[7] 钟莹,杨学志,唐益明,刘灿俊,岳峰. 采用结构自适应块匹配的非局部均值去噪算法[J]. 电子与信息学报,2013,35(12):2908-2915.
Zhong Ying, Yang Xue-zhi, Tang Yi-ming, et al. Non-local Means Denoising Derived from Structure-adapted Block Matching[J]. Journal of Electronics & Information Technology, 2013, 35(12):2908-2915.
[8] 胡新海,欧阳永林,曾庆才,王兴,康敬程. 叠前非局部平均滤波压制随机噪音[J]. 煤田地质与勘探,2014,42(5):87-91.
HU Xinhai, OUYANG Yonglin, ZENG Qingcai, et al. DE-noising data with pre-stack nonlocal means method[J]. Coal Geology & Exploration, 2014, 42(5):87-91.
[9] Tracey, B.H, Miller, E.L. Nonlocal means denoising of ECG signals [J]. IEEE Transactions on Biomedical Engineering, 2012,59(9):2383 –2386.
[10] Mien Van, Heen-Jun kang, Kyoo-Sik Shin. Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition[J], IET Science Measurement & Technology,2014,08(06):571-578.
[11] Case Western Reserve University. Bearing data center[EB/OL].http://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website.2014-1-6.
[12] CLINE J, BILODEAU J, SMITH R. Acoustic wayside identification of freight car roller bearing defects[C]//ASME/IEEE, Joint Railroad Conference, April 15-16, 1998, Philadelphia. Piscataway: IEEE, 1998:79-83.
[13] Wu Z, Huang N E, Ensemble empirical mode decomposition: A noise assisted data analysis method[J]. Adv. Adapt. Data Anal, 2009, 1(1): 1-41.
[14] 王玉静,康守强,张云,等. 基于集合经验模态分解敏感固有模态函数选择算法的滚动轴承状态识别方法[J]. 电子与信息学报,2014,36(3):595-600.
Wang Yujing, Kang Shouqiang, Zhang Yun,et al. Condition recognition method of rolling bearing based on ensemble empirical mode decomposition sensitive intrinsic mode function selection algorithm[J]. Journal of Electronics & Information Technology, 2014, 36(3): 595-600.
[15] 王红军,万鹏. 基于 EEMD 和小波包变换的早期故障敏感特征获取[J]. 北京理工大学学报,2013,33(9):945-950. Wang Hongjun, Wan Peng. Sensitive features extraction of early fault based on EEMD and WPT[J]. Transactions of Beijing Institute of Technology, 2013, 33(9): 945-950.