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Rolling bearing feature frequency extraction using extreme average envelope decomposition

  • Precision Measurement and Signal Processing
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Abstract

The vibration signal contains a wealth of sensitive information which reflects the running status of the equipment. It is one of the most important steps for precise diagnosis to decompose the signal and extracts the effective information properly. The traditional classical adaptive signal decomposition method, such as EMD, exists the problems of mode mixing, low decomposition accuracy etc. Aiming at those problems, EAED(extreme average envelope decomposition) method is presented based on EMD. EAED method has three advantages. Firstly, it is completed through midpoint envelopment method rather than using maximum and minimum envelopment respectively as used in EMD. Therefore, the average variability of the signal can be described accurately. Secondly, in order to reduce the envelope errors during the signal decomposition, replacing two envelopes with one envelope strategy is presented. Thirdly, the similar triangle principle is utilized to calculate the time of extreme average points accurately. Thus, the influence of sampling frequency on the calculation results can be significantly reduced. Experimental results show that EAED could separate out single frequency components from a complex signal gradually. EAED could not only isolate three kinds of typical bearing fault characteristic of vibration frequency components but also has fewer decomposition layers. EAED replaces quadratic enveloping to an envelope which ensuring to isolate the fault characteristic frequency under the condition of less decomposition layers. Therefore, the precision of signal decomposition is improved.

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Authors and Affiliations

  1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 20072, China

    Kunju Shi, Shulin Liu, Chao Jiang & Hongli Zhang

Authors
  1. Kunju Shi
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  2. Shulin Liu
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  3. Chao Jiang
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  4. Hongli Zhang
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Corresponding author

Correspondence to Shulin Liu.

Additional information

Supported by National Natural Science Foundation of China(Grant Nos. 51175316, 51575331)

SHI Kunju, born in 1984, is currently a PhD candidate at School of Mechatronics Engineering and Automation, Shanghai University, China. His research interests include signal processing and fault diagnosis.

LIU Shulin, born in 1963, is currently a professor and a doctoral supervisor at School of Mechatronics Engineering and Automation, Shanghai University, China. He received his PhD degree from Harbin Institute of Technology, China, in 2003. His major research direction is complex equipment fault diagnosis.

JIANG Chao, born in 1987, is currently a PhD candidate at School of Mechatronics Engineering and Automation, Shanghai University, China. His research interests include the fault diagnosis method based on signal processing.

ZHANG Hongli, born in 1985, is currently a postdoctor at Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, China. He received his PhD degree from Shanghai University, China, in 2014. His research interests include intelligent fault diagnosis and pattern recognition.

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Shi, K., Liu, S., Jiang, C. et al. Rolling bearing feature frequency extraction using extreme average envelope decomposition. Chin. J. Mech. Eng. 29, 1029–1036 (2016). https://doi.org/10.3901/CJME.2015.1106.132

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  • DOI: https://doi.org/10.3901/CJME.2015.1106.132

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