NDLI: Extraction of fault feature values for piston compressor based on bispectrum analysis

Content Provider	IEEE Xplore Digital Library
Author	Yanbing Zhou Yibing Liu Keguo Yan Hong Xu
Copyright Year	2009
Description	Author affiliation: Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Beijing, 102206, China (Yanbing Zhou; Yibing Liu; Keguo Yan; Hong Xu)
Abstract	Vibration signals of running piston compressor ordinarily include cyclical impulsion components. And they have the typical characteristics of super-Gaussian signals. When the compressor has weak local faults, it probably causes energy and phase changes of the vibration signals. However, the classic analytical methods which base on low-order statistics are often difficult to reflect the changes caused by weak faults, and they are unable to accurately extract fault feature information. So they are not easy to accurately classify faults. This paper propose to use bispectrum which belongs to high-order statistical analysis (HOSA) to analyze vibration signals of the piston compressor, suppress the signal Gaussian random noise of the signals, highlight the vibration changes caused by weak faults, and extract fault feature value from the energy which is from sensitive regions of bispectrum. Through vibration signal analysis of three different types of piston compressor faults, it proves that using bispectrum analysis method can show the differences among various types of vibration failures more clearly. It has high sensitivity when we distinguish failures. Meanwhile bispectrum analysis can effectively suppress Gaussian noise from the measurement vibration signals, and provide more effective characteristic parameters for automatic fault diagnosis.
Starting Page	4822
Ending Page	4826
File Size	9243764
Page Count	5
File Format	PDF
ISBN	9781424426928
DOI	10.1109/ICMA.2009.5246416
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Pistons Signal analysis Statistical analysis Data mining Gaussian noise Vibration measurement Information analysis Failure analysis Noise measurement piston compressor HOSA bispectrum fault diagnosis feature values extraction
Content Type	Text
Resource Type	Article

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