Condition Monitoring of Rotodynamic Machinery Using Acoustic Emission and Fuzzy C-Mean Clustering Technique-Reference-Cited by-同舟云学术

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Condition Monitoring of Rotodynamic Machinery Using Acoustic Emission and Fuzzy C-Mean Clustering Technique

Published:2001 Issue: Volume: Page:49-56
ISSN:
Container-title:Condition Monitoring and Diagnostic Engineering Management
language:
Short-container-title:

Author:

Kaewkongka T,Au YH Joe,Rakowski RT,Jones BE

Publisher

Elsevier

Reference6 articles.

1. Adaptive prognostics for rolling element bearing condition;Li;Mechanical Systems and Signal Processing,1999

2. Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery;Kaewkongka;IEEE Instrumentation and Measurement Technology,2001

3. Acoustic emission analysis for bearing condition monitoring;James Li;Wear,1995

4. Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings;Tandon;Tribology International,1992

5. Fuzzy logic with engineering applications;Ross,1995

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2. Facile wear failure monitoring of commercial bearing alloys using I-kaz method;Engineering Failure Analysis;2013-12

3. Wear Monitoring of Connecting Rod Bearing via Air-Borne Method Analyzed by Using I-Kaz^TM Multi Level Value;Advanced Materials Research;2012-01

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