인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Early detection of incipient faults in three-phase induction motors is crucial to enhance system reliability and to minimize unplanned operational interruptions in industrial environments. Traditional diagnostic techniques often struggle to detect incipient faults, especially under fluctuating load conditions and may require complex signal processing or multiple sensors. The paper introduces a method for early detection of faults in three-phase induction motors using Wavelet Kernel-enabled convolutional neural networks (CNNs). The proposed system accurately identifies stator interturn faults in single or multiple phases and broken rotor bar faults, even under varying operating conditions such as load variations. By employing 14 mother wavelets as convolution filters, the method effectively extracts critical features from stator current signatures, streamlining the fault detection and classification process. This technique leverages the deep structures of CNNs to autonomously learn features from current signals, achieving a notable accuracy of above 97% in tests with both simulated model and two different hardware motor setup. The experimental result shows that it is capable of detecting as low as 1–2% of stator interturn fault with varying impedance in short circuit path as well as one broken rotor bar fault. Overall, the proposed method proves to be a powerful tool for the early diagnosis of incipient faults in induction motors with high degree of reliability and effectiveness.
#Rotor (electric)
#Stator
#Induction motor
#Wavelet
#Computer science
#Convolution (computer science)
#Kernel (algebra)
#Fault (geology)
#Convolutional neural network
#Pattern recognition (psychology)
#Artificial neural network
#Artificial intelligence
#Engineering
#Mathematics
#Geology
#Seismology
#Electrical engineering
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