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
Link To Document :
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