TY - JOUR
T1 - Data Mining Applications to Fault Diagnosis in Power Electronic Systems
T2 - A Systematic Review
AU - Moradzadeh, Arash
AU - Mohammadi-Ivatloo, Behnam
AU - Pourhossein , Kazem
AU - Anvari-Moghaddam, Amjad
PY - 2022
Y1 - 2022
N2 - Early fault detection in power electronic systems (PESs) to maintain reliability is one of the most important issues that has been significantly addressed in recent years. In this article, after reviewing various works of literature based on fault detection in PESs, data mining-based techniques including artificial neural network, machine learning, and deep learning algorithms are introduced. Then, the fault detection routine in PESs is expressed by introducing signal measurement sensors and how to extract the feature from them. Finally, based on studies, the performance of various data mining methods in detecting PESs faults is evaluated. The results of evaluations show that the deep learning-based techniques given the ability of feature extraction from measured signals are significantly more effective than other methods and as an ideal tool for future applications in the power electronics industry are introduced.
AB - Early fault detection in power electronic systems (PESs) to maintain reliability is one of the most important issues that has been significantly addressed in recent years. In this article, after reviewing various works of literature based on fault detection in PESs, data mining-based techniques including artificial neural network, machine learning, and deep learning algorithms are introduced. Then, the fault detection routine in PESs is expressed by introducing signal measurement sensors and how to extract the feature from them. Finally, based on studies, the performance of various data mining methods in detecting PESs faults is evaluated. The results of evaluations show that the deep learning-based techniques given the ability of feature extraction from measured signals are significantly more effective than other methods and as an ideal tool for future applications in the power electronics industry are introduced.
KW - Circuit faults
KW - Data mining
KW - Deep learning
KW - Fault detection
KW - Fault diagnosis
KW - Machine learning algorithms
KW - Power electronic systems
KW - Reliability
KW - artificial neural network
KW - deep learning
KW - fault detection
KW - fault tolerant
KW - machine learning
KW - reliability
KW - power electronic systems (PESs)
KW - Artificial neural network (ANN)
UR - http://www.scopus.com/inward/record.url?scp=85120553078&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2021.3131293
DO - 10.1109/TPEL.2021.3131293
M3 - Review article
SN - 0885-8993
VL - 37
SP - 6026
EP - 6050
JO - I E E E Transactions on Power Electronics
JF - I E E E Transactions on Power Electronics
IS - 5
ER -