Vibration signal analysis for condition monitoring of puffer-type high-voltage circuit breakers using wavelet transform

2011 ◽  
Vol 7 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Noppadol Charbkaew ◽  
Thanapong Suwanasri ◽  
Teratam Bunyagul ◽  
Armin Schnettler
Keyword(s):  
Wavelet Transform ◽  
Condition Monitoring ◽  
High Voltage ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Circuit Breakers ◽  
Vibration Signal Analysis

scholarly journals Vibration Condition Monitoring: Latest Trend and Review

2015 ◽  
Vol 773-774 ◽  
pp. 139-143
Author(s):  
K.H. Hui ◽  
L.M. Hee ◽  
M. Salman Leong ◽  
Ahmed M. Abdelrhman
Keyword(s):  
Decision Making ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Machine Learning Techniques ◽  
Vibration Condition ◽  
The Past ◽  
Analysis Methods ◽  
Vibration Signal Analysis

Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

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