Condition monitoring and diagnosis of rotating machinery by Gram-Charlier expansion of vibration signal

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 follo...

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Main Authors: Toyota, T., Niho, T., Peng Chen
Format: Conference Proceeding
Language:English
Subjects:
Condition monitoring
Density functional theory
Feature extraction
Gaussian distribution
Intelligent structures
Intelligent systems
Knowledge engineering
Machine intelligence
Machinery
Rolling bearings
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creator Toyota, T.
Niho, T.
Peng Chen
description 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.
doi_str_mv 10.1109/KES.2000.884106
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subjects Condition monitoring
Density functional theory
Feature extraction
Gaussian distribution
Intelligent structures
Intelligent systems
Knowledge engineering
Machine intelligence
Machinery
Rolling bearings
title Condition monitoring and diagnosis of rotating machinery by Gram-Charlier expansion of vibration signal
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