Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Domestic Research Trends on Artificial Intelligence-Based Prognostics and Health Management

인공지능 기반 건전성 예측 및 관리에 관한 국내 연구 동향 분석

  • Ye-Eun Jeong (Department of Industrial Systems Engineering, Kyonggi University Graduate School) ;
  • Yong Soo Kim (Department of Industrial Systems Engineering, Kyonggi University)
  • 정예은 (경기대학교 일반대학원 산업시스템공학과) ;
  • 김용수 (경기대학교 산업시스템공학과)
  • Received : 2023.04.04
  • Accepted : 2023.04.29
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

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, established classification criteria, and analyzed research trends based on classified fields and techniques. Results: Analysis of 125 domestic studies revealed a greater emphasis on machinery in both diagnosis and prognosis, with more papers dedicated to diagnosis. various algorithms were employed, including CNN for image diagnosis and frequency analysis for signal data. LSTM was commonly used in prognosis for predicting failures and remaining life. Different industries, data types, and objectives required diverse AI techniques, with GAN used for data augmentation and GA for feature extraction. Conclusion: As studies on AI-based PHM continue to grow, selecting appropriate algorithms for data types and analysis purposes is essential. Thus, analyzing research trends in AI-based PHM is crucial for its rapid development.

Keywords

Acknowledgement

본 연구는 2023학년도 경기대학교 대학원 연구원장학생 장학금 지원에 의하여 수행되었음.

References

  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. https://doi.org/10.33162/JAR.2021.6.21.2.173
  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. https://doi.org/10.5050/KSNVE.2020.30.3.286
  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. https://doi.org/10.7839/KSFC.2021.18.1.024
  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. https://doi.org/10.3744/SNAK.2022.59.2.125
  5. Baik, J. W. 2019. AI Techniques for Prognostics and Health Management. Journal of Applied Reliability 19(3):243-255. https://doi.org/10.33162/JAR.2019.09.19.3.243
  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. https://doi.org/10.33162/JAR.2021.12.21.4.341
  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. https://doi.org/10.33097/JNCTA.2022.06.05.775
  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. https://doi.org/10.5302/J.ICROS.2020.20.0005
  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. https://doi.org/10.5370/KIEE.2019.68.5.662
  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. https://doi.org/10.5302/J.ICROS.2020.20.0124
  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. https://doi.org/10.7855/IJHE.2018.20.2.057
  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. https://doi.org/10.1016/j.engappai.2020.103678
  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. https://doi.org/10.14775/ksmpe.2021.20.09.035
  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. https://doi.org/10.33162/JAR.2022.12.22.4.342
  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. https://doi.org/10.5370/KIEE.2020.69.11.1732
  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. https://doi.org/10.5302/J.ICROS.2020.20.0121
  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. https://doi.org/10.5370/KIEE.2022.71.1.225
  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. https://doi.org/10.5050/KSNVE.2022.32.4.337
  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. https://doi.org/10.7315/CDE.2022.010
  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. https://doi.org/10.7837/kosomes.2021.27.7.1088
  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. https://doi.org/10.7315/CDE.2020.358
  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. https://doi.org/10.7776/ASK.2020.39.4.263
  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. https://doi.org/10.7840/kics.2022.47.6.862
  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. https://doi.org/10.5762/KAIS.2018.19.1.75
  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. https://doi.org/10.14775/ksmpe.2020.19.12.021
  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.  https://doi.org/10.14801/jkiit.2022.20.6.129
  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. https://doi.org/10.3795/KSME-A.2019.43.3.169
  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. https://doi.org/10.7736/JKSPE.021.117
  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. https://doi.org/10.7736/JKSPE.022.021
  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. https://doi.org/10.3795/KSME-B.2020.44.5.341
  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. https://doi.org/10.5391/JKIIS.2020.30.1.80
  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. https://doi.org/10.3796/KSFOT.2020.56.4.384
  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. https://doi.org/10.37727/jkdas.2020.22.4.1473
  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. https://doi.org/10.5050/KSNVE.2018.28.1.057
  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. https://doi.org/10.7469/JKSQM.2022.50.4.647
  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. https://doi.org/10.7735/ksmte.2021.30.6.513
  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. https://doi.org/10.5573/ieie.2022.59.1.124
  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. https://doi.org/10.12815/kits.2019.18.6.155
  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. https://doi.org/10.7315/CDE.2021.386
  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. https://doi.org/10.5762/KAIS.2021.22.4.611
  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. https://doi.org/10.14775/ksmpe.2019.18.9.029
  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.  https://doi.org/10.35873/AJMAHS.2018.8.3.053
  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. https://doi.org/10.7469/JKSQM.2023.51.1.119
  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. https://doi.org/10.7736/JKSPE.021.002
  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. https://doi.org/10.6109/JKIICE.2022.26.2.199
  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. https://doi.org/10.7734/COSEIK.2021.34.1.25
  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. https://doi.org/10.5391/JKIIS.2021.31.3.185
  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. https://doi.org/10.7469/JKSQM.2017.45.4.649
  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. https://doi.org/10.5762/KAIS.2022.23.10.118
  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. https://doi.org/10.5050/KSNVE.2021.31.1.047
  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. https://doi.org/10.5370/KIEE.2021.70.5.764
  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. https://doi.org/10.17958/KSMT.22.5.202010.871
  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. https://doi.org/10.5762/KAIS.2022.23.7.229
  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. https://doi.org/10.7315/CDE.2018.202
  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. https://doi.org/10.7315/CDE.2022.550
  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. https://doi.org/10.14775/ksmpe.2021.20.12.085
  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. https://doi.org/10.5302/J.ICROS.2022.22.0090
  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. https://doi.org/10.5370/KIEE.2019.68.10.1214
  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. https://doi.org/10.5762/KAIS.2020.21.11.53
  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. https://doi.org/10.7840/kics.2021.46.6.941
  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. https://doi.org/10.5762/KAIS.2021.22.9.147
  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. https://doi.org/10.5050/KSNVE.2021.31.5.563
  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. https://doi.org/10.5762/KAIS.2022.23.2.518
  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. https://doi.org/10.5391/JKIIS.2022.32.2.133
  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. https://doi.org/10.7837/kosomes.2021.27.1.127
  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. https://doi.org/10.5762/KAIS.2022.23.12.877
  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. https://doi.org/10.5916/jkosme.2019.43.6.455
  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. https://doi.org/10.5050/KSNVE.2020.30.2.129
  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. https://doi.org/10.5391/JKIIS.2022.32.4.275
  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. https://doi.org/10.7782/JKSR.2021.24.7.619
  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. https://doi.org/10.33162/JAR.2022.3.22.1.048
  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. https://doi.org/10.7469/JKSQM.2022.50.3.459
  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.  https://doi.org/10.21289/KSIC.2022.25.2.169
  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. https://doi.org/10.23023/JPT.2020.8.3.010
  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. https://doi.org/10.17661/JKIIECT.2019.12.3.242
  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. https://doi.org/10.22865/JITA.2020.17.4.315
  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. https://doi.org/10.5370/KIEE.2021.70.1.096
  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. https://doi.org/10.5050/KSNVE.2022.32.6.535
  97. Moon, H. C., Noh, W. S., Ryu, H. S., and Doh, J. Y. 2022. Deep Neural Network-Based Reliability Assessment on Fatigue Life of PLA Specimens Considering Uncertainty of Additive Manufacturing. Journal of Applied Reliability 22(1):37-47.
  98. Moon, K. Y., Kim, H. J., Hwang, S. Y., and Lee, J. H. 2022. Comparison of Prediction Accuracy Between Classification and Convolution Algorithm in Fault Diagnosis of Rotatory Machines at Varying Speed. Journal of Korean Navigation and Port Research 46(3):280-288.
  99. Nam, J. I. and Park, H. J. 2020. A Neural Network based Fault Detection and Classification System Using Acoustic Measurement. Journal of the Korean Society of Manufacturing Technology Engineers 29(3):10-215. https://doi.org/10.7735/ksmte.2020.29.3.210
  100. Oh, S. T., Kim, H. W., Cho, S. H., You, J. H., Kwon, Y. S., Ra, W. Sang., and Kim, Y. K. 2020. Development of a Compressed Deep Neural Network for Detecting Defected Electrical Substation Insulators using a Drone. Journal of institute of control robotics and systems 26(11):884-890. https://doi.org/10.5302/J.ICROS.2020.20.0117
  101. Park, H. J․, Cho, S. H․, Jang, K. H․, Seol, J. W., Kwon, B. G., Kwon, J. Y., and Choi, J. H. 2020. Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data. Journal of Applied Reliability 20(4):332-342.
  102. Park, J. H., Seok, J. H., Cheon, K. M., and Hur, J. W. 2020. Machine Learning Based Failure Prognostics of Aluminum Electrolytic Capacitors. Journal of the Korean Society of Manufacturing Process Engineers 19(11):94-101. https://doi.org/10.14775/ksmpe.2020.19.11.094
  103. Park, J. I. and Kang, U. G. 2022. Design of particulate matter reduction algorithm by learning failure patterns of PHM-based air conditioning facilites. Journal of The Korea Society of Computer and Information, 27(7):83-92. https://doi.org/10.9708/JKSCI.2022.27.07.083
  104. Park, J. W., Kim, W., Jung, B. C., Moon, S. J., and Yang, S. J. 2021. SVM-based Damage Detection Technique Using Smart Valves in a Pipeline System. Journal of the Korean Society of Mechanical Technology 45(11):1019-1028.
  105. Park, S. H., Do, J. S., Choi, S. D., and Hur, J. W. 2021. Fault Diagnosis of Drone Using Machine Learning. Journal of the Korean Society of Manufacturing Process Engineers 20(9):28-34. https://doi.org/10.14775/ksmpe.2021.20.09.028
  106. Park, S. M., Ko, J. H., Song, S. G., Park, S. J., and Son, N. R. 2020. Fault Diagnosis Algorithm of Electronic Valve using CNN-based Normalized Lissajous Curve. Journal of Korean Society of Industry Convergence 23(5):825-833. 
  107. Seo, C. Y., Suh, Y. J., and Kim, D. J. 2020. Study on Fault Detection of a Gas Pressure Regulator Based on Machine Learning Algorithms. Journal of The Korea Society of Computer and Information 25(4):19-27. https://doi.org/10.9708/JKSCI.2020.25.04.019
  108. Shim, S. B., Choi, S. I., Kong, S. M., and Lee, S. W. 2020. Adversarial learning for underground structure concrete crack detection based on semi-supervised semantic segmentation. Journal of Korean Tunnelling and Underground Space Association 22(5):515-528.
  109. Shim, S. B., Choi, S. I., Kong, S. M., and Lee, S. W. 2021. Deep learning algorithm of concrete spalling detection using focal loss and data augmentation. Journal of Korean Tunnelling and Underground Space Association 23(4):253-263. https://doi.org/10.9711/KTAJ.2021.23.4.253
  110. Shim, S. B., Chun, C. J., and Ryu, S. K. 2019. Road Surface Damage Detection based on Object Recognition using Fast R-CNN. The Journal of The Korea Institute of Intelligent Transportation Systems 18(2):104-113. https://doi.org/10.12815/kits.2019.18.2.104
  111. Shin, Y. S. and Min, K. W. 2021. Decentralized Structural Diagnosis and Monitoring System for Ensemble Learning on Dynamic Characteristics. Journal of the Computational Structural Engineering Institute of Korea 34(4):183-189. https://doi.org/10.7734/COSEIK.2021.34.4.183
  112. Sim, K. C., Lee, K. S., and Byun, S. H. 2022. A study on fault diagnosis of marine engine using a neural network with dimension-reduced vibration signals. The Journal of the Acoustical Society of Korea 41(5):492-499. https://doi.org/10.7776/ASK.2022.41.5.492
  113. So, M. S. and Shin, J. H. 2019. Performance Degradation Prediction of Industrial Equipment using Machine Learning. Korean Journal of Computational Design and Engineering 24(3):268-279. https://doi.org/10.7315/CDE.2019.268
  114. Son, M. J., Kim. Y. G., Noh, S. C., Kim, M. H., and Kim, K. M. 2022. Exploring the Application of CBM+ in an ISR Weapon System, Thermal Observation Device. Journal of Applied Reliability 22(3):240-247. https://doi.org/10.33162/JAR.2022.9.22.3.240
  115. Song, H. B. and Jeong, Jun. 2019. The Implementation of Smart Motor Diagnosis. Journal of Korean Institute of Information Technology 17(4):27-36. https://doi.org/10.14801/jkiit.2019.17.4.27
  116. Song, Y. U. and Baek, S. J. 2021. Real-Time Fault Detection in Discrete Manufacturing Systems Via LSTM Model based on PLC Digital Control Signals. Journal of Korean Society of Industrial and Systems Engineering 44(2):115-123. https://doi.org/10.11627/jkise.2021.44.2.115
  117. Udeoguw, C. U., Caliwag, A. C., and Lim, W. S. 2022. Remaining Useful Life Prediction for Supercapacitors Using an Optimized End-to-End Deep Learning Approach. The Journal of Korean Institute of Communications and Information Sciences 47(3):482-491. https://doi.org/10.7840/kics.2022.47.3.482
  118. Udurumew, M., Udeogu, C. U., Caliwag, A. C., and Lim, W. S. 2022. Synthetic Data Generation Using GAN for RUL Prediction of Supercapacitors. The Journal of Korean Institute of Communications and Information Sciences 47(3):492-500. https://doi.org/10.7840/kics.2022.47.3.492
  119. Wang, D. H., Lee, J. H., Kim, S. J., Kim, M. C., and Lee, I. S. 2022. Fault Diagnosis of Induction Motor using an Ensemble Method of Decision Tree and Multilayer Neural Network. Journal of Korean Institute of Intelligent Systems 20(3):47-55.
  120. Yang, J. W., Lee, Y. D., and Koo, I. S. 2020. Timely Sensor Fault Detection Scheme based on Deep Learning. The Journal of The Institute of Internet, Broadcasting and Communication 20(1):163-169.
  121. Yang, J. W., Lee, Y. D., and Koo. I. S. 2018. Sensor Fault Detection Scheme based on Deep Learning and Support Vector Machine. The Journal of The Institute of Internet, Broadcasting and Communication 18(2): 185-195. https://doi.org/10.7236/JIIBC.2018.18.2.185
  122. Yi. K. Y., Jeong, S. J., and Seo, K. S. 2020. Embedded Deep Learning System for Defects Detection. The Transactions of the Korean Institute of Electrical Engineers 69(2):325-330. https://doi.org/10.5370/KIEE.2020.69.2.325
  123. Yoo, S. J., Jang, D. S., Park, J. W., and Lee, J. K. 2021. Fault Diagnosis of Hydraulic Solenoid Valves using Artificial Intelligence. Journal of The Korean Society for Fluid Power and Construction Equipments 18(4):92-91.
  124. Yoo, S. W., Shin, Y. B., and Shin, D. I. 2020. Comparison of the Machine Learning Models Predicting Lithium-ion Battery Capacity for Remaining Useful Life Estimation. Journal of the Korean Institute of Gas 24(6):91-97. https://doi.org/10.7842/KIGAS.2020.24.6.91
  125. Yoo, Y. S., Kim, D. H., Kim, S., and Hur, J. W. 2020. Fault Prognostics of a SMPS based on PCA-SVM. Journal of the Korean Society of Manufacturing Process Engineers 19(9):47-52. https://doi.org/10.14775/ksmpe.2020.19.09.047
  126. Yoon, I. G., Min, H. R., and Lee, S. 2022. Automatic Detection of the Dominant Frequency in Rotating Facilities. Journal of Mechanical Science and Technology 46(5):495-502.
  127. Yoon, S. J., Lee, M. Y., Lee, J. H., Lee, S. H., and Na, J. C. 2021. Fault Diagnosis Using Artificial Intelligence for the Spindle of Machine Tools. Transactions of the Korean Society of Mechanical Engineers 45(5):401-408. https://doi.org/10.3795/KSME-A.2021.45.5.401
  128. Yoon, Y. A., Jung, J. H., Lim, J. H., Chang, T. W., and Kim, Y. S. 2020. A study on Data Preprocessing for Developing Remaining Useful Life Predictions based on Stochastic Degradation Models Using Air Craft Engine Data. Journal of Korean Society of Industrial and Systems Engineering 43(2):48-55. https://doi.org/10.11627/jkise.2020.43.2.048
  129. Yoon, Y. A., Lee, S. H., and Kim, Y. S. 2021. A Study on the Remaining Useful Life Prediction Performance Variation based on Identification and Selection by using SHAP. Journal of Korean Society of Industrial and Systems Engineering 44(4):1-11. https://doi.org/10.11627/jksie.2021.44.4.001
  130. Yun, J. P., Kim, M. S., Koo, G. W., and Shin, C. 2019. Fault Diagnosis and Analysis Based on Transfer Learning and Vibration Signals. IEMEK Journal of Embedded Systems and Applications 14(6):287-294. https://doi.org/10.14372/IEMEK.2019.14.6.287
  131. Yun, Y. R., Kim, S. G., Cho, S. H., and Choi, J. H. 2019. Neural Network based Aircraft Engine Health Management using C-MAPSS Data. Journal of Aerospace System Engineering 13(6):17-25. https://doi.org/10.20910/JASE.2019.13.6.17