연구동향 분석

협동 로봇 예지보전을 위한 모션 결함 검출 = Motion-Fault Detection for Predictive Maintenance of Collaborative Robot

박예슬 2022년

논문상세정보
인용/피인용
' 협동 로봇 예지보전을 위한 모션 결함 검출 = Motion-Fault Detection for Predictive Maintenance of Collaborative Robot' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 고장 진단
  • 딥 러닝
  • 스마트 팩토리
  • 예지보전
  • 협동 로봇
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
2,222 0

0.0%

' 협동 로봇 예지보전을 위한 모션 결함 검출 = Motion-Fault Detection for Predictive Maintenance of Collaborative Robot' 의 참고문헌

  • `` Variational inference for on-line anomaly detection in high-dimensional time series
  • `` Variance of energy residual ( VER ) : An efficient method for planetary gear fault detection under variable ? speed conditions
    Park , Jungho vol . 453 , pp . 253 ? 267 [2019]
  • `` Unsupervised anomaly detection of industrial robots using sliding-window convolutional variational autoencoder
    Chen , Tingting 8 (47072-47081 [2020]
  • `` Toothwise fault identification for a planetary gearbox based on a health data map
    Ha , Jong M. vol . 65 , no . 7 , pp . 5903 ? 5912 [2017]
  • `` The industrial internet of things ( IIoT ) : An analysis framework
    Boyes , Hugh 101 (1-12 . [2018]
  • `` Research on remaining useful life prediction of rolling element bearings based on time-varying Kalman filter
    Cui , Lingli 2858-2867 . [2019]
  • `` Railway rolling bearing fault diagnosis based on multi ? scale intrinsic mode function permutation entropy and extreme learning machine classifier
    Yao , Dechen vol . 8 , no.10 , pp . 1687814016676157 [2016]
  • `` Model-free robot anomaly detection
  • `` Industrial Robot Rotate Vector Reducer Fault Detection Based on Hidden Markov Models .
  • `` Deep transfer network with joint distribution adaptation : A new intelligent fault diagnosis framework for industry application
    Han , Te vol . 97 , pp . 269 ? 281 , [2020]
  • `` A two-stage approach for the remaining useful life prediction of bearings using deep neural networks
    Xia , Min 3703-3711 . [2018]
  • `` A robust and convex metric for unconstrained optimization in statistical model calibration ? probability residual ( PR )
    Oh , Hyunseok vol . 60 , no . 3 , pp . 1171 ? 1187 [2019]
  • `` A resilience measure formulation that considers sensor faults
    Yoo , Minji [2019]
  • `` A recurrent neural network ? based health indicator for remaining useful life prediction of bearings
    Guo , Liang , vol . 240 , pp . 98 ? 109 [2017]
  • `` A positive energy residual ( PER ) based planetary gear fault detection method under variable speed conditions
    Park , Jungho vol . 117 , pp . 347 ? 360 [2019]
  • `` A new paradigm of cloud ? based predictive maintenance for intelligent manufacturing
    Wang , Jinjiang vol . 28 , no . 5 pp . 1125 ? 1137 [2017]
  • `` A methodology and experimental implementation for industrial robot health assessment via torque signature analysis
    Izagirre , Unai 10.21 ( [2020]
  • `` A fault diagnosis approach for rolling element bearings based on RSGWPT ? LCD bilayer screening and extreme learning machine
    Tong , Qingbin 5 , pp . 5515 ? 5530 [2017]
  • `` A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM : Shallow and deep learning .
    Hamadache , Moussa 125-151 . [2019]
  • `` A Bayesian approach for a damage growth model using sporadically measured and heterogeneous on ? site data from a steam turbine
    Choi , Woosung 184 , pp . 137 ? 150 [2019]
  • [8] Ha, Jong M., et al. "Classification of operating conditions of wind turbines for a class–wise condition monitoring strategy." Renewable energy 103, pp. 594–605, 2017.
    [2017]
  • [7] Rezaeipanah, Amin, Hamed Nazari, and Gholamreza Ahmadi. "A Hybrid Approach for Prolonging Lifetime of Wireless Sensor Networks Using Genetic Algorithm and Online Clustering." Journal of Computing Science and Engineering, vol. 13, no. 4, pp. 163–174, 2019.
    [2019]
  • [6] Ansari, Fazel, et al. "Autodidact: introducing the concept of mutual learning into a smart factory industry 4.0." Proceedings of 15th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2018), pp.61-68, Oct. 2018.
  • [51] “Dobot Magician Lite”, copyright by DOBOT.nu 2021. Available: https://www.dobot.nu/en/product/dobot-magician-lite/
  • [50] “Niryo One - An accessible educational 6 axis robotic arm, just for you – Niryo”, Niryo, 2020. [Online]. Available: https://niryo.com/niryo-one/. [Accessed: 15 Oct 2019].
  • [4] Duarte, Jos A. Caldeira, Joo C. Taborda A. Craveiro, and Toms Pedro Trigo. "Optimization of the preventive maintenance plan of a series components system." International Journal of Pressure Vessels and Piping 83.4 (2006): 244-248.
    [2006]
  • [49] Pereira, Joao, and Margarida Silveira. "Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention." 2018 17th IEEE international conference on machine learning and applications (ICMLA). IEEE, 2018.
    [2018]
  • [48] Kriegel, Hans-Peter, Matthias Schubert, and Arthur Zimek. "Angle-based outlier detection in high-dimensional data." Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008.
    [2008]
  • [47] Khalastchi, Eliahu, et al. "Online data-driven anomaly detection in autonomous robots." Knowledge and Information Systems 43.3 (2015): 657-688.
    [2015]
  • [44] Lu, Haodong, et al. "GAN-based Data Augmentation Strategy for Sensor Anomaly Detection in Industrial Robots." IEEE Sensors Journal (2021).
  • [43] Kanazawa, Akira, Jun Kinugawa, and Kazuhiro Kosuge. "Incremental Learning of Spatial-Temporal Features in Human Motion Patterns with Mixture Model for Planning Motion of a Collaborative Robot in Assembly Lines."€2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019.
    [2019]
  • [42] Munawar, Asim, Phongtharin Vinayavekhin, and Giovanni De Magistris. "Spatio-temporal anomaly detection for industrial robots through prediction in unsupervised feature space." 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017.
    [2017]
  • [40] Kim, Yunhan, et al. "Phase-based time domain averaging (PTDA) for fault detection of a gearbox in an industrial robot using vibration signals." Mechanical Systems and Signal Processing 138 (2020): 106544.
    [2020]
  • [3] Barlow, Richard, and Larry Hunter. "Optimum preventive maintenance policies." Operations research 8.1 (1960): 90-100.
    [1960]
  • [39] Xie, Yuan, and Tao Zhang. "Fault diagnosis for rotating machinery based on convolutional neural network and empirical mode decomposition." Shock and Vibration, 2017.
    [2017]
  • [38] Zhang, Xiaoyuan, Yitao Liang, and Jianzhong Zhou. "A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM." Measurement, vol. 69, pp. 164–179, 2015.
    [2015]
  • [37] Mbo'o, Christelle Piantsop, and Kay Hameyer. "Fault diagnosis of bearing damage by means of the linear discriminant analysis of stator current features from the frequency selection." IEEE Transactions on Industry Applications, vol. 52, no. 5, pp. 3861–3868, 2016.
    [2016]
  • [36] Antonelli, Dario, and Sergey Astanin. "Qualification of a collaborative human–robot welding cell." Procedia CIRP, vol. 41, pp. 352–357, 2016.
    [2016]
  • [35] Rosenstrauch, Martin J., and Jorg Kruger. "Safe human–robot–collaboration–introduction and experiment using ISO/TS 15066." 2017 3rd International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2017.
    [2017]
  • [34] ISO/TS 15066:2016 Robots and robotic devices — Collaborative robots
  • [33] Shi, Junchuan, et al. "Remaining useful life prediction of bearings using ensemble learning: The impact of diversity in base learners and features." Journal of Computing and Information Science in Engineering 21.2 (2021): 021004.
  • [2] Li, Zhe, Yi Wang, and Ke–Sheng Wang. "Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario." Advances in Manufacturing, vol. 5, no. 4, pp.377–387, 2017.
    [2017]
  • [28] Qiao, Guixiu, and Brian A. Weiss. "Quick health assessment for industrial robot health degradation and the supporting advanced sensing development." Journal of manufacturing systems 48 (2018): 51-59.
    [2018]
  • [27] Algburi, Riyadh Nazar Ali, and Hongli Gao. "Health assessment and fault detection system for an industrial robot using the rotary encoder signal." Energies 12.14 (2019): 2816.
    [2019]
  • [25] Zhang, Hepeng, and Yong Deng. "Weighted belief function of sensor data fusion in engine fault diagnosis." Soft computing, vol. 24, no. 3, pp.2329–2339, 2020.
    [2020]
  • [17] Zheng, Jinde, Haiyang Pan, and Junsheng Cheng. "Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines." Mechanical Systems and Signal Processing, vol. 85, pp. 746–759, 2017.
    [2017]
  • [16] Karoly, Artur Istvan, Jozsef Kuti, and P‰ter Galambos. "Unsupervised real–time classification of cycle stages in collaborative robot applications." 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2018.
    [2018]
  • [15] Cheng, Fangzhou, et al. "High–Accuracy Unsupervised Fault Detection of Industrial Robots Using Current Signal Analysis." 2019 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2019.
    [2019]
  • [14] Jaber, Alaa Abdulhady, and Robert Bicker. "Fault diagnosis of industrial robot gears based on discrete wavelet transform and artificial neural network." Insight–Non–Destructive Testing and Condition Monitoring, vol. 58, no. 4, pp. 179–186, 2016.
    [2016]
  • [12] Kim, Yunhan, et al. "Phase–based time domain averaging (PTDA) for fault detection of a gearbox in an industrial robot using vibration signals." Mechanical Systems and Signal Processing, vol. 138, pp. 106544, 2020.
    [2020]
  • [11] Xue, Song, and Ian Howard. "Torsional vibration signal analysis as a diagnostic tool for planetary gear fault detection." Mechanical Systems and Signal Processing, vol. 100, pp. 706–728, 2018.
    [2018]