DocumentCode
2630771
Title
Condition monitoring and diagnosis of rotating machinery by Gram-Charlier expansion of vibration signal
Author
Toyota, Toshio ; Niho, Tomoya ; Chen, Peng
Author_Institution
Fac. of Comput. Sci. & Syst. Eng., Kyushu Inst. of Technol., Fukuoka, Japan
Volume
2
fYear
2000
fDate
2000
Firstpage
541
Abstract
Here we present the new robust condition monitoring and diagnosis method based on the 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 vibration signal follows the normal distribution in time domain. This method can lead to the robust failure diagnosis without any prior knowledge concerning vibration characteristics corresponding to specific failure to be detected
Keywords
condition monitoring; fault diagnosis; knowledge based systems; mechanical engineering computing; statistical analysis; vibration measurement; condition monitoring; diagnosis; statistical hypothesis; vibration characteristics; vibration signal; Condition monitoring; Density functional theory; Feature extraction; Gaussian distribution; Intelligent structures; Intelligent systems; Knowledge engineering; Machine intelligence; Machinery; Rolling bearings;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
Conference_Location
Brighton
Print_ISBN
0-7803-6400-7
Type
conf
DOI
10.1109/KES.2000.884106
Filename
884106
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