Failure detection and diagnosis of rotating machinery by orthogonal expansion of density function of vibration signal

Author

Toyota, Toshio ; Niho, Tomoya ; Chen, Peng

Author_Institution

Fac. of Comput. Sci. & Syst. Eng., Kyushu Inst. of Technol., Fukuoka, Japan

fYear

1999

fDate

1-3 Feb 1999

Firstpage

886

Lastpage

891

Abstract

The authors present a new robust failure detection and diagnosis method based on a statistical hypothesis on vibration characteristics of the rotating machines in good condition. The hypothesis is that if the machine is in good condition, its probability density function of the vibration signal follows the normal distribution in time domain. This method based on the hypothesis for characteristics of vibration of good condition can lead to high precision failure diagnosis without any prior knowledge concerning to vibration characteristics corresponding to specific failure to be detected

Keywords

failure analysis; fault diagnosis; machine testing; machine theory; normal distribution; probability; reliability; failure detection; failure diagnosis; normal distribution; orthogonal expansion; probability density function; rotating machinery; time domain; vibration characteristics; vibration signal; Density functional theory; Feature extraction; Gaussian distribution; Machinery; Probability density function; Rolling bearings; Rotating machines; Signal analysis; Signal processing; Time domain analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Environmentally Conscious Design and Inverse Manufacturing, 1999. Proceedings. EcoDesign '99: First International Symposium On

Conference_Location

Tokyo

Print_ISBN

0-7695-0007-2

Type

conf

DOI

10.1109/ECODIM.1999.747733

Filename

747733