[논문]시분할 CNN-LSTM 기반의 시계열 진동 데이터를 이용한 회전체 기계 설비의 이상 진단

김민기

doi:10.9717/kmms.2022.25.11.1547

시분할 CNN-LSTM 기반의 시계열 진동 데이터를 이용한 회전체 기계 설비의 이상 진단
Anomaly Diagnosis of Rotational Machinery Using Time-Series Vibration Data Based on Time-Distributed CNN-LSTM 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.25 no.11, 2022년, pp.1547 - 1556

김민기 (Dept. of Computer Science, Gyeongsang National University, Engineering Research Institute)

Abstract ▼ AI-Helper

As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

주제어

참고문헌 (23)

K. Choi. J. Yi, C. Park, and S. Yoon, "Deep Learning for Anomaly Detection in TimeSeries Data: Review, Analysis, and Guidelines," IEEE Access, Vol. 9, pp. 120043-120065, 2021.

상세보기
B. Samanta and K.R. Al-Balushi, "Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features," Mechanical Systems and Signal P rocessing, Vol. 17, No. 2, pp. 317-328, 2003.

상세보기
G. Hong and D. Suh, "Supervised-LearningBased Intelligent Falut Diagnosis for Mechanical Equipment," IEEE Access, Vol. 9, pp. 116147-116162, 2021.

상세보기
K. Lee, C. Vununu, K. Moon, S. Lee, and K. Kwon, "Automatic Machine Fault Diagnosis System Using Discrete Wavelet Transform and Machine Learning," J ournal of Korea Multimedia Society, Vol. 20, No. 8, pp. 1299-1311, 2017.

원문보기 상세보기
G. Lee, M. Jung, and M. Song, "Unsupervised Anomaly Detection of the Gas Turbine Operation via Convolutional Auto-Encoder," Proceedings of the IEEE International Conference on Prognostics and Health Management, pp. 1-6, 2020.
C. Zhang, J. Liu, W. Chen, J. Shi, M. Yao, X. Yan, N. Xu, and D. Chen, "Unsupervised Anomaly Detection Based on Deep Autoencoding and Clustering," Security and Communication Networks, Vol. 2021, pp. 1-8, 2021.

상세보기
Z. Chen, C.K. Yeo, B.S. Lee, and C.T. Lau, "Autoencoder-Based Network Anomaly Detection,'' Proceedings of the Wireless Telecommunications Symposium, pp. 1-5, 2018.
R. Chalapathy and S. Chawla, "Deep Learning for Anomaly Detection: A Survey," arXiv Preprint, arXiv:1901.03407, pp. 1-50, 2019
D.H. Pandya, S.H. Upadhyay, and S.P. Harsha, "Fault Diagnosis of Bearing with Supervised Machine Learning Techniques," Proceedings of the International Conference on Innovations in Design and Manufacturing," pp. 1-5, 2012.
S.R. Kumar, D.B. Phavithraa, and R.G. Gayathri, "Supervised Machine Learning Based Anomaly Detection and Diagnosis in Grid Connected Photovoltaic Systems," Proceedings of the International Conference on Combinatorial and Optimization, pp. 1-15, 2021.
Y. He and J. Zhao, "Temporal Convolutional Networks for Anomaly Detection in Time Series," Proceedings of the IOP Conference Series: Journal of Physics, Vol. 1213, pp. 1-6, 2019.
L. Shen, Z. Li, and J.T. Kwok, "Timeseries Anomaly Detection Using Temporal Hierarchical One-Class Network," Proceedings of the Neural Information Processing Systems, pp. 1-11, 2020.
P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, "Long Short Term Memory Networks for Anomaly Detection in Time Series," Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 89-94, 2015.
L. Yu, J. Qu, F. Gao, and Y. Tian, "A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM," Shock and Vibration, Vol. 2019, pp. 1-10, 2019.

상세보기
C.-J. Huang and P.-H. Kuo, "A Deep CNNLSTM Model for Particulate Matter(PM 2.5 ) Forecasting in Smart Cities," Sensors, Vol. 18, pp. 1-22, 2020.

상세보기
Guidelines for AI data construction and utilization: vibration/current data of machinery, https://aihub.or.kr/aihubdata/data/view.do?curr Menu115&topMenu100&aihubDataSerealm&dataSetSn238 (accessed November 1, 2022).
V. Hariharan and P. Srinivasan, "New Approach of Classification of Rolling Element Bearing Fault Using Artificial Neural Network," Journal of Mechanical Engineering, Vol. 40, No. 2, pp. 119-130, 2009.

상세보기
J. Cho and L. Lee, "Cleaning Noises from Time Series Data with Memory Effects," Journal of The Korea Society of Computer and Information, Vol. 25, No. 4, pp. 37-45, 2020.
I. Botunac, A. Panjkota, and M. Matetic, "The Importance of Time Series Data Filtering for Predicting the Direction of Stock Market Movement Using Neural Networks," Proceedings of the International Symposium on Intelligent Manufacturing and Automation, Vol. 30, pp. 886-891, 2019.
J. Donahue, L.A. Hendricks, M. Rohrbach, S. Venugopalan, S. Guadarrama, K. Saenko, and T. Darrell, "Long-Term Recurrent Convolutional Networks for Visual Recognition and Description," arXiv Preprint, arXiv:1411.4389, pp. 1-14, 2015.
Case Western Reserve University Bearing Data Center, Seeded Fault Test Data, https://engineering.case.edu/bearingdatacenter (accessed November 1, 2022).
J. Seo, J. Park, J. Yoo, and H. Park, "Anomaly Detection System in Mechanical Facility Equipment: Using Long Short-Term Memory Variational Autoencoder," Journal of Korean Society for Quality Management, Vol. 49, No. 4, pp. 581-594, 2021.

원문보기 상세보기
J. Seo, A Study on the Anomaly Detection using Machine Facility Data: Focused on Supervised Machine Learning, Yonsei University Master's thesis, 2021.

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증

시분할 CNN-LSTM 기반의 시계열 진동 데이터를 이용한 회전체 기계 설비의 이상 진단
Anomaly Diagnosis of Rotational Machinery Using Time-Series Vibration Data Based on Time-Distributed CNN-LSTM 원문보기

Abstract ▼ AI-Helper

주제어

참고문헌 (23)

이 논문을 인용한 문헌

저자의 다른 논문 :

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

연합인증

시분할 CNN-LSTM 기반의 시계열 진동 데이터를 이용한 회전체 기계 설비의 이상 진단 Anomaly Diagnosis of Rotational Machinery Using Time-Series Vibration Data Based on Time-Distributed CNN-LSTM 원문보기

Abstract ▼ AI-Helper

주제어

참고문헌 (23)

이 논문을 인용한 문헌

저자의 다른 논문 :

김민기 (23)

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

시분할 CNN-LSTM 기반의 시계열 진동 데이터를 이용한 회전체 기계 설비의 이상 진단
Anomaly Diagnosis of Rotational Machinery Using Time-Series Vibration Data Based on Time-Distributed CNN-LSTM 원문보기