Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy–logic inference

2009 ◽  
Vol 36 (2) ◽  
pp. 3785-3794 ◽  
Author(s):  
Jian-Da Wu ◽  
Chuang-Chin Hsu
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Vibration Signal ◽  
Discrete Wavelet ◽  
Fuzzy Logic Inference ◽  
Logic Inference

Pendeteksian Misalignment Menggunakan Multi Level Transformasi Wavelet Haar dan Coiflet pada Motor Induksi

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
P.P.S Saputra
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motor ◽  
High Frequency ◽  
High Efficiency ◽  
Vibration Signal ◽  
Normal Operation ◽  
Discrete Wavelet ◽  
High Frequency Signal ◽  
Motor Operation

Currently induction motors are widely used in industry due to strong construction, high efficiency, and cheap maintenance. Machine maintenance is needed to prolong the life of the induction motor. As studied, bearing faults may account for 42% -50% of all motor failures. In general it is due to manufacturing faults, lack of lubrication, and installation errors. Misalignment of motor is one of the installation errors. This paper is concerned to simulation of discrete wavelet transform for identifying misalignment in induction motor. Modelling of motor operation is introduced in this paper as normal operation and two variations of misalignment. For this task, haar and coiflet discrete wavelet transform in first level until fifth level is used to extract vibration signal of motor into high frequency of signal. Then, energy signal and other signal extraction gotten from high frequency signal is evaluated to analysis condition of motor. The results show that haar discrete wavelet transform at thirth level can identify normal motor  and misalignment motor conditions well

Assessment of Power Quality based on Fuzzy Logic and Discrete Wavelet Transform for Nonstationary Disturbances

2010 ◽  
Author(s):  
Pampa Sinha ◽  
Sudipta Nath ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
Suchismita Roy
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Discrete Wavelet

scholarly journals Classification of fetal abnormalities based on CTG signal

2014 ◽  
Vol 11 (2) ◽  
pp. 681-689
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Discrete Wavelet ◽  
Artificial Neural

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was transformed using transform domains Discrete Wavelet Transform(DWT) in order to obtain the system features .At the last stage the approximation coefficients result from the Discrete Wavelet Transform were fed to the Artificial Neural Networks and to the Fuzzy Logic, then compared between two results to obtain the best for classifying fetal heart rate.

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