An Evaluation of Linear Time Frequency Distribution Analysis for VSI Switch Faults Identification

2017 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
M.F. Habban ◽  
M. Manap ◽  
A.R. Abdullah ◽  
M.H. Jopri ◽  
T. Sutikno
Keyword(s):  
Fault Detection ◽  
Frequency Distribution ◽  
Linear Time ◽  
Harmonic Distortion ◽  
Voltage Source ◽  
Distribution Analysis ◽  
Voltage Source Inverter ◽  
Time Frequency ◽  
S Transform ◽  
Time Frequency Distribution

This paper present an evaluation of linear time frequency distribution analysis for voltage source inverter system (VSI). Power electronic now are highly demand in industrial such as manufacturing, industrial process and semiconductor because of the reliability and sustainability. However, the phenomenon that happened in switch fault has become a critical issue in the development of advanced. This causes problems that occur study on fault switch at voltage source inverter (VSI) must be identified more closely so that problems like this can be prevented. The TFD which is STFT  and S-transform method are analyzed the switch fault of VSI.  To identify the VSI switches fault, the parameter of fault signal such as instantaneous of average current, RMS current, RMS fundamental current, total waveform distortion, total harmonic distortion and total non-harmonic distortion can be estimated from TFD. The analysis information are useful especially for industrial application in the process for identify the switch fault detection. Then the accuracy of both method, which mean STFT and S-transform are identified by the lowest value of mean absolute percentage error (MAPE). In addition, the S-transform gives a better accuracy compare with STFT and it can be implement for fault detection system.

Comparison of Open and Short-Circuit Switches Faults Voltage Source Inverter (VSI) Analysis Using Time-Frequency Distributions

2015 ◽  
Vol 752-753 ◽  
pp. 1164-1169 ◽  
Author(s):  
M. Manap ◽  
Nur Sumayyah Ahmad ◽  
Abdul Rahim Abdullah ◽  
Norhazilina Bahari
Keyword(s):  
Short Circuit ◽  
Harmonic Distortion ◽  
Automated System ◽  
Voltage Source ◽  
Identification System ◽  
Voltage Source Inverter ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Detection Analysis ◽  
Detection And Identification

Voltage source inverter (VSI) plays an important roles in electrical drive systems. Consistently, expose to hash environmental condition, the lifespan of the electronic component such as insulated-gate bipolar transistor (IGBT) may shorten and many faults related to the inverter especially switches can be occur. The present of VSI switches faults causing equipment failure and increased the cost of manufacturing process. Therefore, faults detection analysis is mandatory to identify the VSI switches faults. This paper presents the analysis of VSI switches faults using time-frequency distributions (TFDs) which are short times Fourier transform (STFT) and spectrogram. From time-frequency representation (TFR) obtained by using the TFDs, parameters of the faults signal are estimated such as instantaneous of average, root mean square (RMS), fundamental, Total Waveform Distortion (TWD), Total Harmonics Distortion (THD) and Total non-Harmonic Distortion (TnHD) of current signals. Then, based on the characteristics of the faults calculated from the signal parameters, VSI switches faults can be detected and identified. The performance of TFD for the faults analysis is also demonstrated to obtain the best TFD for switches faults detection and identification system. The results show that, STFT is the best technique to classify and identify VSI switches faults and can be implemented for automated system.

Voltage Source Inverter Fault Detection System Using Time Frequency Distribution

2015 ◽  
Vol 761 ◽  
pp. 88-92
Author(s):  
Nur Sumayyah Ahmad ◽  
M. Mustafa ◽  
Abdul Rahim Abdullah ◽  
N.A. Abidullah ◽  
Norhazilina Bahari
Keyword(s):  
Fault Detection ◽  
Detection System ◽  
Voltage Source ◽  
Maintenance Cost ◽  
Voltage Source Inverter ◽  
Time Frequency ◽  
Three Phase ◽  
Detailed Simulation ◽  
Open Switch ◽  
Fault Mode

Open-switch and short-switch in a three-phase voltage source inverter (VSI) have a possibility to fault due to problems of switching devices.Any failure of the system in these applications may incur a cost and risk human live. Therefore, knowledgeon the fault mode behaviour of an inverter is extremely important from the standpoint of system design improvement, protection and fault detection. This paper presents detailed simulation results on condition monitoring and fault behaviour of VSI. The results obtained from the developed monitoring system allows user to identify the fault current. The developed system showed the capability in detecting the performance of VSI as well as identifying the characteristics of type of faults. This system provides a precaution and early detection of fault, thus reduces high maintenance cost and prevent critical fault from happening.

scholarly journals Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study

2019 ◽  
Vol 9 (16) ◽  
pp. 3433 ◽  
Author(s):  
Rami Alazrai ◽  
Saifaldeen AL-Rawi ◽  
Hisham Alwanni ◽  
Mohammad I. Daoud
Keyword(s):  
Frequency Distribution ◽  
Support Vector ◽  
Distribution Analysis ◽  
Eeg Signals ◽  
Cold Pain ◽  
Time Frequency ◽  
Evaluation Procedures ◽  
Pain Detection ◽  
Frequency Features ◽  
Time Frequency Distribution

Detecting pain based on analyzing electroencephalography (EEG) signals can enhance the ability of caregivers to characterize and manage clinical pain. However, the subjective nature of pain and the nonstationarity of EEG signals increase the difficulty of pain detection using EEG signals analysis. In this work, we present an EEG-based pain detection approach that analyzes the EEG signals using a quadratic time-frequency distribution, namely the Choi–Williams distribution (CWD). The use of the CWD enables construction of a time-frequency representation (TFR) of the EEG signals to characterize the time-varying spectral components of the EEG signals. The TFR of the EEG signals is analyzed to extract 12 time-frequency features for pain detection. These features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To evaluate the performance of our proposed approach, we have recorded EEG signals for 24 healthy subjects under tonic cold pain stimulus. Moreover, we have developed two performance evaluation procedures—channel- and feature-based evaluation procedures—to study the effect of the utilized EEG channels and time-frequency features on the accuracy of pain detection. The experimental results show that our proposed approach achieved an average classification accuracy of 89.24% in distinguishing between the no-pain and pain classes. In addition, the classification performance achieved using our proposed approach outperforms the classification results reported in several existing EEG-based pain detection approaches.

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