Angular domain intensity feature extraction method for fault vibration signals of hydraulic pump under variable rotating speed condition

被引：0
|
作者：
Liu S. [1 ,2 ,3 ]
Wang Z. [1 ,2 ]
Jiang W. [1 ,2 ]
Li X. [4 ]
Lu M. [3 ]
Lu Z. [3 ]
机构：
[1] Hebei Provincial Key Lab of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao
[2] MOE Key Lab of Advanced Forging & Stamping Technology and Science, Yanshan University, Qinhuangdao
[3] Jiangsu Tianming Machinery Co., Ltd., Lianyungang
[4] China Railway Construction Heavy Industry Group Co., Ltd., Changsha
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2020年 / 39卷 / 17期
关键词：
Angular intensity feature; Feature extraction; Hydraulic pump; Variable rotating speed; Vibration signal;
D O I：
10.13465/j.cnki.jvs.2020.17.019
中图分类号：
学科分类号：
摘要：
Extracting feature information sensitive to faults is the key to improve the correctness of hydraulic pump condition assessment, due to severe lack of sensitive feature information of hydraulic pump faults under variable rotating speed condition, the assessment correctness rate is much low. Here, a new concept of angular domain intensity feature and a new feature extraction method for hydraulic pump slipper wear faults were proposed. Non-stationary vibration signals in time domain were converted into stationary ones in angular domain with the order analysis method. According to the definition of vibration intensity and the calculation method in frequency domain, unilateral amplitude value spectra and harmonic frequencies of stationary vibration signals in angular domain were obtained, and then calculation formulas of angular domain intensity feature factors for three vibration signals including displacement, velocity and acceleration were derived. Taking inner edge wear fault of hydraulic pump slipper as an example, the angular domain intensity feature factor of fault vibration signals was extracted under variable rotating speed condition. Qualitative and quantitative contrastive analyses were performed for this feature factor and the order energy one. It was shown that the angular domain intensity feature factor is more sensitive to deterioration development of hydraulic pump slipper wear fault. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：142 / 149
页数：7
相关论文
共 20 条
  • [1] SUN Jian, LI Hongru, Method on feature extraction basd on composite spectrum and relative entropy fusion, Journal of Mechanical Engineering, 53, 24, pp. 96-103, (2017)
  • [2] JIANG Wanlu, LIU Yunjie, ZHU Yong, Resarch on wavelet demodulation and twice EMD based fault identification method and its application, Chinese Journal of Scientific Instrument, 34, 5, pp. 1131-1138, (2013)
  • [3] LIU Yujiao, YAO Entao, XU Hongzhuan, Fault diagnosis of hydraulic hump based on particle filtering and autoregressive spectrum, Chinese Journal of Scientific Instrument, 33, 3, pp. 561-567, (2012)
  • [4] TIAN Y, LU C, WANG Z, Et al., Fault diagnosis based on LMD-SVD and information-geometric support vector machine for hydraulic pumps, Transactions of the Canadian Society for Mechanical Engineering, 39, 3, pp. 569-580, (2015)
  • [5] WANG C, WANG Z, MA J, Et al., Fault diagnosis for hydraulic pump based on EEMD-KPCA and LVQ, The International Conference "Vibroengineering-2014", (2014)
  • [6] WANG Tianyang, LI Jianyong, CHENG Weidong, The instantaneous fault characteristic frequency and fault characteristic order template based fault diagnosis algorithm for rolling bearing under time-varying rotational speed, Journal of Vibration Engineering, 28, 6, pp. 1006-1014, (2015)
  • [7] YANG Y, WANG H, CHENG J, Et al., A fault diagnosis approach for roller bearing based on VPMCD under variable speed condition, Measurement, 46, 8, pp. 2306-2312, (2013)
  • [8] ABBOUD D, ANTONI J, SIEG-ZIEBA S, Et al., Envelope analysis of rotating machine vibrations in variable speed conditions: a comprehensive treatment, Mechanical Systems & Signal Processing, 84, pp. 200-226, (2017)
  • [9] TIAN Y, MA J, LU C, Et al., Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine, Mechanism & Machine Theory, 90, pp. 175-186, (2015)
  • [10] ZHAO Ming, LIN Jing, Adaptive dynamic information extraction and state evaluation for machinery under varying speeds, Journal of Mechanical Engineering, 8, (2015)
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