연구동향 분석

CNN 기반 딥러닝을 이용한 베어링 고장 진단의 정확도 및 계산 복잡도 분석

박재현 김철홍 2022년

논문상세정보
인용/피인용
' CNN 기반 딥러닝을 이용한 베어링 고장 진단의 정확도 및 계산 복잡도 분석' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Complexity
  • Deep Learning
  • Fault Detection
  • Fault Diagnosis
  • bearing
  • 고장 진단
  • 고장검출
  • 딥 러닝
  • 베어링
  • 복잡도
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
4,050 0

0.0%

' CNN 기반 딥러닝을 이용한 베어링 고장 진단의 정확도 및 계산 복잡도 분석' 의 참고문헌

  • 회전기계 결함신호 진단을 위한 신호처리 기술 개발
    안병현 [2014]
  • 진동신호 특성 예측및 분류를 통한 회전체 고장진단 방법
    김동환 [2014]
  • 스펙트로그램 이미지를 이용한 CNN 기반 자동화 기계 고장 진단 기법
    강경원 [2020]
  • 경량 딥러닝 기술동향
    이용주 [2019]
  • Very Deep Convolutional Networks for Large-Scale Image Recognition
  • SqueezeNet : AlexNet-Level Accuracy with 50x Fewer Parameters And <0. 5MB Model Size>
  • Singularity analysis using continuous wavelet transform for bearing fault diagnosis
    Qiao Sun [2002]
  • ShuffleNetV2 : Practical Guidelines for Efficient CNN Architecture Design
    Ningning Ma [2018]
  • Rolling bearing fault detection using continuous deep belief network with locally linear embedding
  • Rolling Bearing Fault Diagnosis Based on STFT Deep Learning and Sound Signals
    Hongmei Liu [2016]
  • MobileNets : Efficient Convolutional Neural Networks for Mobile Vision Application
  • LeNet
    Y. Lecun [1998]
  • LSTM 오토인코더를 이용한 라디에이터 고장진단 사례연구
    이정근 [2020]
  • Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine
  • Intelligent Diagnosis Method using Acoustic Emission Signals for Bearing under Complex Working Conditions
  • Fault diagnosis of rotating machinery using an intelligent order tracking system
  • Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN
  • Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features
  • Fault Diagnosis of Rotary Machine Bearings Under Inconsistent Working Conditions
  • Fault Diagnosis of Bearings with Variable Rotational Speeds Using Convolutional Neural Networks
    Viet Tra [2019]
  • Efficient Sport Videos Classification via Convolutional Neural Network
    노먼 칸 [2020]
  • Diagnosis of bearing defects under variable speed conditions using energy distribution maps of acoustic emission spectra and convolutional neural networks
    Viet Tra [2018]
  • Dempster-Shafer evidence theory for multi-bearing faults diagnosis
  • Deep Residual Learning for Image Recognition
    Kaiming He [2016]
  • CNN에 기반한 실제 환경에서의 색상 및 문자 인식
    박현철 [2016]
  • Bearing fault identification and classification with convolutional neural network
  • An overview of gradient descent optimization algorithms
  • Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram
  • A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks
    Zhuyun Chen [2020]
  • A Hybrid Feature Selection Scheme for Reducing Diagnostic Performance Deterioration Caused by Outlines in Data-Driven Diagnostics
    M. Kang [2016]