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인공지능 기반 건전성 예측 및 관리에 관한 국내 연구 동향 분석
Analysis of Domestic Research Trends on Artificial Intelligence-Based Prognostics and Health Management 원문보기

品質經營學會誌 = Journal of Korean society for quality management, v.51 no.2, 2023년, pp.223 - 245  

정예은 (경기대학교 일반대학원 산업시스템공학과) ,  김용수 (경기대학교 산업시스템공학과)

Abstract ▼ AI-Helper 아이콘AI-Helper

Purpose: This study aim to identify the trends in AI-based PHM technology that can enhance reliability and minimize costs. Furthermore, this research provides valuable guidelines for future studies in various industries Methods: In this study, I collected and selected AI-based PHM studies, establish...

주제어

#Prognostics Health Management   #Fault Diagnosis   #Prediction   #Artificial Intelligence   #Deep Learning  

표/그림 (14)

참고문헌 (131)

  1. Ahn, Y. G., Kim, S. J., Kim, D. H., Kim, C. M., and Kim, W. S. 2021. Remaining Life Prediction Using Gas Pipeline？Segmentation Algorithm Based on Decision Tree. Journal of Applied Reliability 21(2):173-180. 

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  2. Back, J. S., Kim, S. W., Lee, S. K., and Lee, C. H. 2020. Conditioning Monitoring in Chain Sprocket Drive Unit？System Based on Artificial Neural Network. Transactions of the Korean Society for Noise and Vibration？Engineering 30(3):286-293. 

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  3. Baek, H. S., Shin, J. H., and Kim, S. J. 2021. Development of AI-Based Condition Monitoring System for Failure？Diagnosis of Excavator's Travel Device. Journal of Drive and Control 18(1):24-30. 

  4. Baek, S. D. and Woo, J. H. 2022. Fault Detection of Propeller of an Overactuated Unmanned Surface Vehicle based？on Convolutional Neural Network. Journal of the Society of Naval Architects of Korea 59(2):125-133. 

  5. Baik, J. W. 2019. AI Techniques for Prognostics and Health Management. Journal of Applied Reliability？19(3):243-255. 

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  6. Chae, S. G., Kim, G. R., Bae, B. Y., and Bae, S. J. 2021. Failure Diagnosis and Prediction for a Thermal Power？Plant Generator using fastICA. Journal of Applied Reliability) (Journal of Applied Reliability 21(4):341-351. 

  7. Cho, J, H. 2022. A Study on the Operation Status Monitoring, Diagnosis and Failure Prediction Algorithm of Smart？ESP. Journal of Next-generation Convergence Technology Association 6(5):775-780. 

  8. Choi, D. J, Han, J. H., Park, S. U, and Hong. S. K. 2020. Motor Fault Diagnosis in Changed Environments using？K-Means and CNN. Journal of Institute of Control, Robotics Systems 26(5):348-354. 

  9. Choi, D. J., Han, J. H., and Hong, S. K. 2019. Real-Time Self-Complement System of Fault Diagnosis for Induction？Motor Using Machine Learning and IoT Technique. The Transactions of the Korean Institute of Electrical？Engineers 68(5):662-669. 

  10. Choi, D. J., Han, J. H., Park, S. H., and Hong, S. K. 2020. Deep Learning Motor Failure Diagnosis System Considering？Small IoT Devices. Journal of institute of control robotics and systems 26(11):900-906. 

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  11. Choi, S. H., and Do, M. S. 2018. Prediction of Asphalt Pavement Service Life using Deep Learning. International？Journal of Highway Engineering 20(2):57-65. 

  12. Fink, O., Wang, Q., Svensen, M., Dersin, P., Lee, W. J., and Ducoffe, M. 2020. Potential, challenges and future？directions for deep learning in prognostics and health management applications. Engineering Applications of？Artificial Intelligence 92:952-1976. 

  13. Go, J. I., Lee, E. Y., Lee, M. J., Choi, S. D., and Hur, J. W. 2021. Corrosion Failure Diagnosis of Rolling Bearing？with SVM. Journal of the Korean Society of Manufacturing Process Engineers 20(9):35-41. 

  14. Goh. Y. J., Kim, G. N., Kim, Y. H., Lee, B., and Kim, K. M. 2020. Diagnosis Method for Stator-Faults in Induction？Motor using Park' s Vector Pattern and Convolution Neural Network. Institute of Korean Electrical and？Electronics Engineers 24(3):883-889. 

  15. Ha, S. and Kim, D. H. 2022. Deep Learning based Semiconductor Wafer Maps Clustering Considering Outliers. Journal？of Applied Reliability 22(4):342-351. 

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  16. Han, D. J., Kim, S. J., and Lee, S, C. 2022. A Realization of Real Time Algorithm for Fault and Health Diagnosis？of Turbofan Engine Components. Journal of The Korean Society Aeronautical and Space Sciences？50(10):717-727. 

  17. Han, J. H., Choi, D. J., Park, S. U., and Hong, S. K. 2020. A Study on the GAN Algorithm Performance Improvement？Method in Motor Failure Diagnosis Using Deep Learning Algorithm. The Transactions of the Korean Institute？of Electrical Engineers 69(11):1732-1739. 

  18. Han, J. H., Choi, D. J., Park, S. W., and Hong, S. K. 2020. DT-CNN based Motor Failure Prediction Considering Outlier Data. Journal of institute of control robotics and systems 26(11):932-939. 

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  19. Han, J. H., Park, S. W., and Hong, S. K. 2022. Performance Evaluation of the Continuos Wavelt Transformation？Data in Motor Fault Diagnosis through XAI Algorithm. The Transactions of the Korean Institute of Electrical？Engineers 71(7):225-232. 

  20. Han, S. B. 2019. Study on the Development of Diagnosis Algorithm for Induction Motor Using Current and Magnetic？Flux Sensors. Journal of IEEE Korea Council 23(4):42-50. 

  21. Hwang, G. Y., Jeong, S. M., and Oh, J. S. 2022. Development of Prediction Algorithm Featuring 1-D CNN for Vehicle？Wheel Nuts. Transactions of the Korean Society for Noise and Vibration Engineering 32(4):337-345. 

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  22. Hwang, S. Y., Lee, J. H., Kim, K. S., Oh, J. W., and Min, C. H. 2020. Development of Real-time Condition Monitoring？System Based on Machine Learning for Winch Equipment of Floating Crane. Journal of Computational Design？and Engineering 25(4):445-454. 

  23. Hyun, D. H. and Lee, S. H. 2022. Defect Detection in Manufacturing System Using Continual Learning. Korean Journal？of Computational Design and Engineering 27(1):10-18. 

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  24. Jang, J. G., Noh, C. M., Kim, S. S., Lee, S. S, and Lee, J. C. 2021. Vibration Data Denoising and Performance？Comparison Using Denoising Auto Encoder Method. Journal of the Korean Society of Marine Environment？& Safety 27(7):1088-1097. 

  25. Jang, J. W., An, H. J., Lee, J. H., and Shin, S. B. 2019. Construction of Faster R-CNN Deep Learning Model for？Surface Damage Detection of Blade Systems. Journal of the Korea Institute for Structural Maintenance and？Inspection 23(7):80-86. 

  26. Jang, M. H., Park, H. S, Kim, J. I., Oh, J. R., and Jun, H. B. 2020. A Case Study on Predicting the Vehicle Failure？Code with Gathered Diagnostic Trouble Code Data. Korean Journal of Computational Design and Engineering？25(4):358-365. 

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  27. Jeon, H. K., Kim, J. S., Kim, B. J., and Kim, W. J. 2022. A study on the fault diagnosis of rotating machine by？machine learning. The Journal of the Acoustical Society of Korea 39(4):263-269. 

  28. Jeon, J. H., Cheon, H. J., Chu, Y. J., and Kim, H. S. 2022. Deep-Learning Based Lithium-ion Battery SOH Estimation？Using Multi-Channel Charging Profile and Discharge Capacity. Journal of Korean Institute of Communications？and Information Scien 47(6):862-869. 

  29. Ju. Y. J., Kim, M. S., Kim, K. S., and Lee, J. H. 2020. Comparison of Machine Learning Algorithms Applied to？Classification of Operating Condition of Rotating Machinery. Journal of Computational Design and Engineering？25(1):77-87. 

  30. Jung, H. and Park, M. S. 2018. A Study of Big data-based Machine Learning Techniques for Wheel and Bearing？Fault Diagnosis. Journal of the Korea Academia-Industrial cooperation Society 19(1):75-84. 

  31. Jung, J. H., Kim, D. H., Kim, C. S, Oh, R. D., and Ahn, J. H. 2020. Intelligent Railway Detection Algorithm Fusing？Image Processing and Deep Learning for the Prevent of Unusual Events. Journal of Internet Computing and？Services 21(4):109-116. 

  32. Jung, J. H., Sun, K. H., and Kim, K. 2021. Fault Diagnosis Method for Excavator Hydraulic Axial Piston Pumps.？Journal of The Korean Society for Fluid Power and Construction Equipments. 18(4):98-103. 

  33. Jung, S. J. and Hur, J. W. 2020. Deep Learning Approaches to RUL Prediction of Lithium-ion Batteries. Journal？of the Korean Society of Manufacturing Process Engineers 19(12):21-27. 

  34. Jung, S. M. and Choi, W. J. 2022. A Study on Deep Learning-based Fault Diagnosis using Vibration Data of Wind？Generato. Journal of Korean Institute of Information Technology 20(6):129-136.？ 

  35. Jung, W. H., Jeong, D. H., Kim, Y. H., Kim, C. H., Lee, H. S., Yu, H. J., Ryu, J. H., and Oh, H. S. 2019. Deep Generative？Models to Overcome an Insufficient Data Problem in Structural Health Diagnosis. Journal of the Korean Society？of Mechanical Engineers 43(3):169-176. 

  36. Kang, M. G,, Hyun, Y. H., and Lee, C.B. 2022. "Deep Learning-Based Analysis for Abnormal Diagnosis of Air？Compressors." Journal of the Korean Society for Precision Engineering 39(3):209-215. 

  37. Kang, M. Y. and Lee, C. B. 2022. Development of Prognostics and Health Management System for Rotating Machine？and Application to Rotary Table. Journal of the Korean Society for Precision Engineering 39(5): 337-343. 

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  38. Kang, S. B., Lee, H. H., Oh, J. S., and Choi, K. S. 2020. Fault Prediction of a Heavy Oil Combined Heat and Power？Boiler Using Machine Learning. The Transactions of the Korean Society of Mechanical Engineers？44(5):341-346. 

  39. Kim, D. H., Kim, S. J., Kim, W. S., and Kim, C. M. 2020. A Generative Adversarial Network based Data Generating？for Estimation of Remaining Life Distribution in Gas Pipes. Journal of Korean Institute of Intelligent Systems？30(1):80-85. 

  40. Kim, D. H., Lee, J. H., Lee, S. B., and Jung, B. K. 2020. Outlier detection of main engine data of a ship using ensemble？method. Journal of the Korean Society of Fisheries and Ocean Technology 56(4):384-394. 

  41. Kim, D. H., Lee, S. B., and Lee, J. H. 2020. Anomaly detection of Vessel Main Engine Big Data using Gaussian？Mixture Model. Journal of the Korean Data Analysis Society 22(4):1473-1489. 

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  42. Kim, H. J., Ha, J. M., Ahn, B. H., Park, D. H., and Choi, B. K. 2018. Failure Classification of Gearbox using Ultrasonic？Signal Characteristic. Transactions of the Korean Society for Noise and Vibration Engineering 28(1):57-63. 

  43. Kim, H. J., Kim, K. S., Hwang, S. Y., and Lee, J H. 2022. The Fault Diagnosis Model of Ship Fuel System Equipment？Reflecting Time Dependency in Conv1D Algorithm Based on the Convolution Network. Journal of Korean？Navigation and Port Research 46(4):367-374. 

  44. Kim, H. S., Ko, D. B., Lee, W. G., and Bae, Y. S. 2022. A Study on Tire Surface Defect Detection Method Using？Depth Image. Proceedings of the Korea Information Processing Society？Conference 11(5):211-220. 

  45. Kim, I. J., Kim, W. S., Kim, J. Y., Chae, H. S., Woo, J. Y., Do, K. M., Lim, S. H., Shin, M. S., Lee, J. E., and Kim,？H. N. 2022. Discovering Essential AI-based Manufacturing Policy Issues for Competitive Reinforcement of？Small and Medium Manufacturing Enterprises. Journal of Korean Society for Quality Management？50(4):647-664. 

  46. Kim, I. S., Lee, M. G., and Jeon, Y. H. 2021. Comparative Analysis of Defect Detection Using YOLO of Deep Learning.？Journal of the Korean Society of Manufacturing Technology Engineers 30(6):514-519. 

  47. Kim, J. H., Shin, J. H., and Kim, T. H. 2022. Low-latency Bearing Fault Diagnosis based on Convolutional LSTM？Model. Journal of The Institute of Electronics and Information Engineers 59(1):124-130. 

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  48. Kim, J. M？, Hyeon, S. G., Chae, J. H., and Do, M. S. 2019. Road Crack Detection based on Object Detection Algorithm？using Unmanned Aerial Vehicle Image. The Journal of The Korea Institute of Intelligent Transportation？Systems 18(6):155-163. 

  49. Kim, J. S., Lee, G. B., Hwang, H. S., Ahn, J. S., Oh, J. R., Jang, M. H., and Jun, H. B. 2021. A Study on DTW-based？RUL Estimation Algorithm of Propulsion Motor: Case Study. Korean Journal of Computational Design and？Engineering 26(4):386-397. 

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  50. Kim, J. T., Seo, Y. W., Lee, S. S., Kim, S. J., and Kim, Y. G. 2021. A Proposal of Remaining Useful Life Prediction？Model for Turbofan Engine based on k-Nearest Neighbor. Journal of the Korea Academia-Industrial cooperation Society 22(4):611-620. 

  51. Kim, J. W., Jang, J. S., Yang, M. S., Kang, J. H., Kim, K. W., Cho, Y. J., and Lee, J. W. 2019. A Study on Fault？Classification of Machining Center using Acceleration Data Based on 1D CNN Algorithm. Journal of the Korean？Society of Manufacturing Process Engineers 18(9):29-35. 

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  52. Kim, J. Y., Jeong, I. G., and Kim, J. M. 2018. Acoustic Emission based early fault detection and diagnosis method？for pipeline. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology？8(3):571-578.？ 

  53. Kim, K. H., Kim, S. M., and Kim, Y. S. 2023. A Study on Optimization of Classification Performance through Fourier？Transform and Image Augmentation. Journal of Korean Society for Quality Management 51(1):119-129. 

  54. Kim, K. W., Kang, J. H., and Park, S. H. 2021. A Machine Learning-Based Signal Analytics Framework for Diagnosing？the Anomalies of Centrifugal Pumps. Journal of the Korean Society for Precision Engineering 38(4): 269-277. 

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  55. Kim, M. H. and Jin, K. H. 2022. Development of a Deep Learning Algorithm for Anomaly Detection of Manufacturing？Facility. Journal of the Korea Institute of Information and Communication Engineering 26(2):199-206. 

  56. Kim, M. J., Cho, H. J., and Kang, C. G. 2022. LSTM-based Anomaly Detection for Screw Air Compressors of Railway？Vehicles. Journal of the Korean Society of Mechanical Engineers 46(2):195-202. 

  57. Kim, N. J., and Bae, Y. C. 2018. Status Diagnosis of Pump and Motor Applying K-Nearest Neighbors. Journal of？The？Korea Institute of Electronic Communication Sciences 13(6):1249-1256. 

  58. Kim, S. I., Noh, Y. J., Kang, Y. J., Park, S. H., and Ahn, B. H. 2021. Fault Classification Model Based on Time？Domain Feature Extraction of Vibration Data. Journal of the Computational Structural Engineering Institute？of Korea 34(1):25-33. 

  59. Kim, S. J. 2021. Application of Fuzzy Logic Based Machine Learning to the Assessment of Failure Probability and？Remaining Useful Life for Corroded Pipes. Journal of Korean Institute of Intelligent Systems 31(3):185-191. 

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  60. Kim, S. J., Choe, B. H., and Kim W. S. 2017. Prognostics for Industry 4.0 and Its Application to Fitness-for-Service？Assessment of Corroded Gas Pipelines. Journal of Korean Society for Quality Management 45(6):649-664. 

  61. Kim, S. M., Lee, H. Y. L., Hwang, I. S., and Hur, J. W. 2022. Analysis of Fault Diagnosis Algorithm for Thermal？Imaging Camera Circuit Board Using Machine Learning. Journal of the Korea Academia-Industrial cooperation？Society 23(10):118-124. 

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  62. Kim, S. W., An, K. H., Back, J. S., Lee, S. K., Lee, C. H., and Kim, P. G. 2021. Health Monitoring of Power Driving？System Using Sound Signal based on Deep Learning. Transactions of the Korean Society for Noise and？Vibration Engineering 31(1):47-56. 

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  63. Kim, W. J. and Kim, S. H. 2021. ANN-Based Diagnostic Method on Multiple Open-Switch Fault for Three-Phase？PWM Converters. The Transactions of the Korean Institute of Electrical Engineers 70(5):764-775. 

  64. Kim, Y. G. and Kim, J. W. 2020. Development of Aigorithm for Predicting the Electrode Life of Spot Welding on？Automotive Steel Plate. Journal of the Korean Society of Mechanical Technology 22(5):871-876. 

  65. Kim, Y. G., Son, M. J., Noh, S. C., and Kim, S. J. 2022. A Study on Prevention of Condensation and Freezing in？Radar Waveguide using Condition Based Maintenance. Journal of the Korea Academia-Industrial cooperation？Society 23(7):229-239. 

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  66. Kim, Y. J., Shin, J. H., Hwang, H. J., and Jun, H. B. 2018. A Study on Prognostics Approach for Estimating the？RUL of Mooring Line. Korean Journal of Computational Design and Engineering 23(3):202-214. 

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  67. Kim. H. J., Hwang, S. Y., Kim, G. S., Kim, K. M., and Lee, J. H. 2022. Performance of Conv1D Model Considering？Both Non-stationarity and the Time Interval Applied to the Condition Diagnosis of Rotary Fuel Pump. Korean？Journal of Computational Design and Engineering 27(4):550-559. 

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  68. Ko, D. H., Choi, W. H., Choi, S. D., and Hur, J. W. 2021. Failure Prognostics of Start Motor Based on Machine？Learning. Journal of the Korean Society of Manufacturing Process Engineers 20(12):85-91. 

  69. Ko, H. C., Seok, K. H., Lee, J. H., Park, J. H., and Kim, S. W. 2022. Application of Virtual Sensors for Fault Detection？and Back-up of Combustion Gas Analyzers in a Steel Plant Furnace. Journal of Institute of Control, Robotics？and Systems 28(8):708-713. 

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  70. Kwak, M. S. and Lee, J. S. 2022. Diagnosis-Based Domain-Adaptive Design Using Data Augmentation and Transfer？Learning. Journal of Mechanical Science and Technology 46(11):975-986. 

  71. Kwon, S. G., Han, D. H., Park. S. Y., and Kim, J. H. 2019. Long Short Term Memory-Based State-of-Health Prediction？Algorithm of a Rechargeable Lithium-Ion Battery for Electric Vehicle. The Transactions of the Korean Institute of Electrical Engineers 68(10):1214-1221. 

  72. Kwon, S. J. and Kim, M. S. 2020. "Flaw Evaluation of Bogie connected Part for Railway Vehicle Based on Convolutional？Neural Network." Journal of the Korea Academia-Industrial cooperation Society 21(11): 53-60. 

  73. Kye, H. S. and Kwon, M. H. 2021. PCA-Based Low-Complexity Anomaly Detectio. The Journal of Korean Institute？of Communications and Information Sciences 46(6):941-955. 

  74. Lee, B. S. 2021. Development of Deep Learning Model to Estimate Clogging of Stormwater Infiltration Filter. Journal？of the Korea Academia-Industrial cooperation Society 22(9):147-155. 

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  75. Lee, C. Hun., Lee, S. K., and Kim, P. I. 2021. Fault Detection and Diagnosis of Chain Transmission System Using？Convolutional Auto-encoder. Transactions of the Korean Society for Noise and Vibration Engineering？31(5):563-573. 

  76. Lee, D. K., Park, J. W., and Cho, S. H, and Lee, J. S. 2022. Maintainability Prediction of Guided Missile based on？Machine Learning using Field Data. Journal of the Korea Academia-Industrial cooperation Society？23(2):518-526. 

  77. Lee, G. H., Lee, Y. D., and Koo, I. S. 2019. An RNN-based Fault Detection Scheme for Digital Sensor. The Journal？of The Institute of Internet, Broadcasting and Communication 19(1):29-35. 

  78. Lee, H. J. and Kim, S. S. 2022. Analysis of Anomaly Diagnosis on Operation Data in Nuclear Power Plant using？Mahalanobis Distance and Random Forest. Journal of Korean Institute of Intelligent Systems 32(2):133-138. 

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  79. Lee, J. G. and Kim, D. H. 2020. Case Study on Fault Diagnosis of Radiator Using LSTM Autoencoder. Journal of？Korean Institute of Next Generation Computing 16(26):17-25. 

  80. Lee, J. H. 2021. Experimental Study on Application of an Anomaly Detection Algorithm in Electric Current Datasets？Generated from Marine Air Compressor with Time-series Features. Journal of the Korean Society of Marine？Environment & Safety 27(1):127-134. 

  81. Lee, J. H., Kwon, M. G., Kim, Y. B., and Hur, J. W. 2022. Failure Diagnostics of Camera Image Sensor For Vehicle？Using CNN. Journal of the Korea Academia-Industrial cooperation Society 23(12):877-884. 

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  82. Lee, J. H., Yoo, S. Y., Shin, S. C., Kang. D. H., Lee. S. S., and Lee, J. C. 2019. Fault diagnosis of bearings using？machine learning algorithm. Journal of the Korean Society of Marine Engineering 43(6):455-462. 

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  83. Lee, N. J., Kim, S. M., Jeong, I. J., Sohn, S. M., and Lee, S. C. 2020. Ensemble Method using Rule-based and？Deep-learning Algorithms for Rotating-machine Diagnostics. The Korean Society for Noise and Vibration？Engineering 30(2):129-135. 

  84. Lee, P. Y., Kwon, S. G., Kang, D. H., Han, S. Y., and Kim, J. H. 2020. SOH Estimation and Feature Extraction using？Principal Component Analysis based on Health Indicator for High Energy Battery. The Transactions of the？Korean Institute of Power Electronics 25(5):376-384. 

  85. Lee, S. H., Kang, S. H., Shin, Y. S., Choi, O. K., Kim, S. J., and Kang, J. Mo. 2022. YOLO-Based Detection of Metal？Surface Defects. Journal of korean institute of Intelligent Systems 32(4):275-285. 

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  86. Lee, S. H., Kim, B., and Lee, H. S. 2021. An Oil Transformer Life Estimation System Using an Autoencoder Based？on a Generative Model. Journal of the korean sosiety for railway 24(7):619-624. 

  87. Lee, S. H., Kim, J. Y., Lee, J. J, Kim, Y. J., Kim, S. K., and Lee, T. H. (2022). A Study on the Development of？Database and Algorithm for Fault Diagnosis for Condition Based Maintenance of Rubber Seal in Ancillary？Equipment of Autonomous Ships. Journal of Applied Reliability 22(1):48-58. 

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  88. Lee, S. H. and Kim, Y. S. 2022. A Pre-processing Process Using TadGAN-based Time-series Anomaly Detection.？Journal of Korean Society for Quality Management 50(3):459-471. 

  89. Lee, S. H., Wesonga, S., and Park, J. S. 2022. Classification of Operating State of Screw Decanter using Video-Based？Optical Flow and LSTM Classifier. Journal of the Korean Society of Industry Convergence 25(2): 169-176.？ 

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  90. Lee, S. I and Ko, D. S. 2020. A Study on the Design of Prediction Model for Safety Evaluation of Partial Discharge.？Journal of platform technology 8(3):10-21. 

  91. Lee, S. Y. and Huh, Y. J. 2021. A Study on Real-Time Defect Detection System Using CNN Algorithm During Scaffold？3D Printing. Journal of the Semiconductor & Display Technology 20(3):125-130. 

  92. Lee, Y. H., Kim, K. J., Lee, S. I., and Kim, D. J. 2019. Seq2Seq model-based Prognostics and Health Management？of Robot Arm. Journal of Korea Institute of Information, Electronics, and Communication Technology？12(3):242-250. 

  93. Lee. Y. K., Hong, S. C., and Hong, J. K. 2020. LSTM-based Drone's Anomal Motor Vibration Detection System.？Journal of Information Technology and Architecture 17(4):315-321. 

  94. Lim, J. K. and Yoon, H. J. 2021. A study on the Feature extraction of the Rolling Stock door using the current？value of the motor and the selection of a failure diagnosis prediction algorithm. The Transactions of the？Korean Institute of Electrical Engineers 70(1):96-101. 

  95. Lim, J. Y., Kim, D. H., Noh, T. W., and Lee, B. K. 2022. Remaining Useful Life Prediction for Litium-Ion Batteries？Using EMD-CNN-LSTM Hybrid Method. The Transactions of the Korean Institute of Power Electronics？27(1):48-55. 

  96. Min, T. H., Park, D. H., Lee, J. J., Seo, S. Y., Kang, S. W., and Choi, B. G. 2022. Feature-based Analysis on Vibration？Signals for Fault Diagnosis of Elevator. Transactions of the Korean Society for Noise and Vibration Engineering？32(6):535-543. 

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