基于改进经验小波变换的机车轴承故障诊断
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摘要
机车轴承在噪声较大的背景下工作,发生故障时,难以有效地提取其故障特征,针对这一问题,提出了经验小波变换(EWT)方法。为克服经验小波变换方法中噪声分量干扰子频带划分的问题,提出一种采用信号时频峭度谱局部极小值划分频带的方法,基于子频带构造正交小波滤波器组对信号进行EWT分解。仿真实验和工程应用表明,改进后的EWT能够较好地克服噪声分量对子频带划分的干扰,有效地分离出机车轴承损伤故障的特征。
Locomotive bearings worked in the background of loud noise, it was difficult to extract the fault characteristics effectively. To solve this problem, an EWT was proposed. In order to overcome the problem of noise component interference sub-band divisions in EWT method, an approach was proposed, which used local minimum on a kind of time-frequency kurtosis spectrum of analyzed signals to make the frequency-band partitions, and orthogonal wavelet filter group was constructed based on the sub-bands to perform EWT decomposition. Simulation experiments and engineering applications show that the improved EWT may better overcome the interference of noise components on sub-band divisions, and effectively extract the defect features of locomotive bearings.
Locomotive bearings worked in the background of loud noise, it was difficult to extract the fault characteristics effectively. To solve this problem, an EWT was proposed. In order to overcome the problem of noise component interference sub-band divisions in EWT method, an approach was proposed, which used local minimum on a kind of time-frequency kurtosis spectrum of analyzed signals to make the frequency-band partitions, and orthogonal wavelet filter group was constructed based on the sub-bands to perform EWT decomposition. Simulation experiments and engineering applications show that the improved EWT may better overcome the interference of noise components on sub-band divisions, and effectively extract the defect features of locomotive bearings.
引文
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[10] YAN Zhonghong,MIYAMOTO A,JIANG Zhongwei,et al. An Overall Theoretical Description of Frequency Slice Wavelet Transform[J]. Mechanical Systems and Signal Processing,2010,24(2):491-507.
[11] 段晨东,高鹏,徐先锋,等. 一种基于时频峭度谱的滚动轴承损伤诊断方法[J]. 机械工程学报,2015,51(11):78-83.DUAN Chendong, GAO Peng, XU Xianfeng,et al. A Diagnosis Method of Rolling Bearing Damage Based on Time-frequency Kurtosis Spectrum[J]. Journal of Mechanical Engineering, 2015, 51(11): 78-83.
[2] 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[J]. 西安交通大学学报,2010,44(3):77-81.WANG Xiaodong, HE Zhengjia, ZI Yanyang. Multi-wavelet Spectral Kurtosis Method for Fault Diagnosis of Rolling Bearings[J]. Journal of Xi’an Jiaotong University, 2010, 44(3): 77-81.
[3] LEI Yaguo, LIN Jing, HE Zhengjia,et al. Application of an Improved Kurtogram Method for Fault Diagnosis of Rolling Element Bearings[J]. Mechanical Systems and Signal Processing, 2011, 25(5):1738-1749.
[4] ZHAO Ming, LIN Jing, XU Xiaoqiang, et al. Multi-fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-based Adaptive Envelope Order Analysis[J].Sensors,2014, 14(11):20320-20346.
[5] LI Zipeng, CHEN Jinglong, ZI Yanyang, et al. Independence-oriented VMD to Identify Fault Feature for Wheel Set Bearing Fault Diagnosis of High Speed Locomotive[J]. Mechanical Systems and Signal Processing, 2017, 85(15):512-529.
[6] GILLES J. Empirical Wavelet Transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
[7] SINGH O, SUNKARIA R K. ECG Signals Denoising via Empirical Wavelet Transform[J]. Australasian Physical and Engineering Sciences in Medicine, 2017, 40(1):219-229.
[8] 王友仁,陈伟,孙灿飞,等.基于能量聚集度经验小波变换的齿轮箱早期微弱故障诊断[J].中国机械工程,2017,28(12):1484-1490.WANG Youren, CHEN Wei, SUN Canfei, et al. Early Weak Fault Diagnosis of Gearboxes Based on Energy Aggregation and EWT[J].China Mechanical Engineering, 2017,28(12):1484-1490.
[9] WANG Dong, ZHAO Yang, YI Cai, et al. Sparsity Guided Empirical Wavelet Transform for Fault Diagnosis of Rolling Element Bearings[J]. Mechanical Systems and Signal Processing, 2018, 101:292-308.
[10] YAN Zhonghong,MIYAMOTO A,JIANG Zhongwei,et al. An Overall Theoretical Description of Frequency Slice Wavelet Transform[J]. Mechanical Systems and Signal Processing,2010,24(2):491-507.
[11] 段晨东,高鹏,徐先锋,等. 一种基于时频峭度谱的滚动轴承损伤诊断方法[J]. 机械工程学报,2015,51(11):78-83.DUAN Chendong, GAO Peng, XU Xianfeng,et al. A Diagnosis Method of Rolling Bearing Damage Based on Time-frequency Kurtosis Spectrum[J]. Journal of Mechanical Engineering, 2015, 51(11): 78-83.