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A feature extraction method based on LMD and its application for fault diagnosis of reciprocating compressor annular valve.

Number: pap. 1304

Author(s) : SHU Y., ZHANG Q., XIE C., et al.

Summary

Taking the reciprocating compressor annular valve as the research object, the vibration signal of the reciprocating compressor ring valve was tested by the accelerometer vibration sensor. Through local mean decomposition (LMD), several PF (Product Function) components corresponding to the signal were obtained. Three characteristics parameter factors of these PF components are extracted, including the skewness coefficient (gi), kurtosis coefficient (qi) and total energy ratio (Ei/E). Then the valve is damaged to various degrees, including sawing the valve plate, removing some springs from the valve and drilling the valve plate. The same analysis on the operating vibration signal of the damaged valve plate was carried out to obtain the corresponding parameter factors of the normal valve. The results show that under the circumstances of valve sawing and part spring removing, the valve vibration signal obtained by the corresponding characteristic parameter factor will reflect the abnormal value of the fault, but the valve disc perforated state is not obvious. The above shows that although the LMD method has some limitations, it can accurately and effectively evaluate the vibration signals of reciprocating compressor valves, and classify the working status and the fault type of the valve, so it is a practical method to study the diagnosis of valve failure.

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Pages: 10

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Details

  • Original title: A feature extraction method based on LMD and its application for fault diagnosis of reciprocating compressor annular valve.
  • Record ID : 30024281
  • Languages: English
  • Source: 2018 Purdue Conferences. 24th International Compressor Engineering Conference at Purdue.
  • Publication date: 2018/07/09

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