• Journal of international Conference on Electrical Machines and Systems
• /
• 제3권2호
• /
• pp.190-199
• /
• 2014

In the last years, fault problem in power electronics has been more and more investigated both from theoretical and practical point of view. The fault problem can cause equipment failure, data and economical losses. And the analyze system require to ensure fault problem and also rectify failures. The current errors on these faults are applied for identified type of faults. This paper presents technique to detection and identification faults in three-phase voltage source inverter (VSI) by using time-frequency distribution (TFD). TFD capable represent time frequency representation (TFR) in temporal and spectral information. Based on TFR, signal parameters are calculated such as instantaneous average current, instantaneous root mean square current, instantaneous fundamental root mean square current and, instantaneous total current waveform distortion. From on results, the detection of VSI faults could be determined based on characteristic of parameter estimation. And also concluded that the fault detection is capable of identifying the type of inverter fault and can reduce cost maintenance.

• 전기학회논문지
• /
• 제57권4호
• /
• pp.729-734
• /
• 2008

Vibroarthrographic(VAG) signals, emitted by human knee joints, are non-stationary and multi-component in nature and time-frequency distributions(TFD) provide powerful means to analyze such signals. The objective of this paper is to classify VAG signals, generated during joint movement, into two groups(normal and patient group) using the characteristic parameters extracted by time-frequency transform, and to evaluate the classification accuracy. Noise within TFD was reduced by singular value decomposition and back-propagation neural network(BPNN) was used for classifying VAG signals. The characteristic parameters consist of the energy parameter, energy spread parameter, frequency parameter, frequency spread parameter by Wigner-Ville distribution and the amplitude of frequency distribution, the mean and the median frequency by fast Fourier transform. Totally 1408 segments(normal 1031, patient 377) were used for training and evaluating BPNN. As a result, the average value of the classification accuracy was 92.3(standard deviation ${\pm}0.9$)%. The proposed method was independent of clinical information, and showed good potential for non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis and chondromalacia patella.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제55권4호
• /
• pp.181-188
• /
• 2006

The purpose of this study is to predict the defibrillation success of a ventricular Fibrillation ECG signal using time-frequency analysis. During CPR, coronary perfusion pressure and electrocardiogram were measured. Parameters extracted from time-frequency domain were served as predictor of resuscitation success. Time frequency distribution(TFD) of ECG signals was estimated from the smoothed pseudo Wigner-Ville distribution(SPWVD). Median frequency, peak frequency, 1/f slope, frequency band ratios$(2{\sim}4Hz,\;4{\sim}6Hz,\;6{\sim}8Hz,\;8{\sim}10Hz,\;10{\sim}12Hz,\;12{\sim}15Hz)$ were extracted from each TFD as function of time. Paired t-test was used to determine the differences in ROSC and non-ROSC groups. In the statistical results, we selected four significant parameters - median frequency, 1/f slope, $2{\sim}4Hz$ band ratio, $8{\sim}10Hz$ band ratio. We made an attempt to predict defibrillation success by combining features extracted from time frequency distribution. Independent t-test was used to determine the differences ROSC and non-ROSC groups. Consequently, we selected four significant parameters-median frequency, 1/f slope, $2{\sim}4Hz$ band ratio, $8{\sim}10Hz$ band ratio. The relationship between coronary perfusion pressure and ECG parameters was analyzed with linear regression analysis. R-square value was 55%. 1/f slope and $8{\sim}10Hz$ band ratio had the significant relationship with coronary perfusion pressure.