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Development of a CCTV Based Smart Safety Management System in Thermal Power Plants

석탄발전산업을 위한 지능형 CCTV 기반 스마트안전관리시스템 개발 연구

  • Song, Ho Jun (College of Industrial Engineering, Sungkyunkwan University) ;
  • Gao, Jianxi (College of Industrial Engineering, Sungkyunkwan University) ;
  • Shin, Wan Seon (College of Systems Management Engineering, Sungkyunkwan University)
  • 송호준 (성균관대학교 산업공학과) ;
  • 고건실 (성균관대학교 산업공학과) ;
  • 신완선 (성균관대학교 시스템경영공학과)
  • Received : 2021.08.04
  • Accepted : 2021.09.08
  • Published : 2021.09.30

Abstract

There has been a steady rate of accident in Coal Thermal Power Plants which have relatively higher chance of mortality. However, neither the systematic view of safety management nor the methodology such as safety factors or system requirements are yet to be studied in detail. Therefore, this study aims to propose a methodology to preemptively deal with safety issues and to secure fact focused responsibility in safety. It consists of two main parts. First, the Safety Measurement Index(SMI) with total 50 factors is proposed by analyzing the key factors that contribute to safety accidents based on failure mode and effect analysis (FMEA) and quality function deployment (QFD). To analyze the safety requirements, index presented by major countries and organizations are discussed. Second, main features of intelligent CCTV are studied to determine their relative importance for the framework of Smart Safety Management System (SSMS). Main features are discussed with four technological steps. Also, QFD was held to analyze to analyze how key technologies deal with Quality Measurement Index(QMI). The research results of this study reveal that scientific approaches could be utilized in integrating CCTV technologies into a smart safety management system in the era of Industry 4.0. Moreover, this reasearch provides an specific approach or methodology for dealing with safety management in Coal Thermal Power Plant.

Keywords

References

  1. Al-Nawashi, M., Al-Hazaimeh, O.M., and Saraee, M., A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments, Neural Computing and Applications, 2017, Vol. 28, No. 1, pp. 565-572.
  2. Bas, E., An integrated quality function deployment and capital budgeting methodology for occupational safety and health as a systems thinking approach: the case of the contruction industry, Accident Analysis & Prevention, 2014, No. 68, pp. 42-56. https://doi.org/10.1016/j.aap.2013.10.005
  3. Bergquist, K. and Abeysekera, J., Quality Function Deployment(QFD) - A means for developing usable products, International Journal of Industrial Ergonimics, 1996, No. 16, Vol. 4, pp. 269-275. https://doi.org/10.1016/0169-8141(95)00051-8
  4. Bhattacharjee, P., Dey, V., and Mandal, U.K., Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model, Safety Science, 2020, Vol. 132, 104967. https://doi.org/10.1016/j.ssci.2020.104967
  5. Chae, M. and Cho, J.H., Platform of ICT-based environmental monitoring sensor data for verifying the reliability, Journal of Platform Technology, 2021, Vol. 9, No. 1, pp. 23-31.
  6. Chang, K.H., Evaluate the orderings of risk for failure problems using a more general RPN methodology, Microelectronics Reliability, 2009, Vol. 49, No. 12, pp. 1586-1596. https://doi.org/10.1016/j.microrel.2009.07.057
  7. Cho, J.N., A study of development for national occupational health and safety indicators, Korea Occupational Safety & Health Administration, 2015.
  8. Cocca, P., Marciano, F., & Alberti, M., Video surveillance systems to enhance occupational safety: A case study, Safety Science, 2016, Vol. 26, pp. 140-148.
  9. Cocca, P., Marciano, F., and Rossi, D., Assessment of biomechanical risk at work: practical approaches and tools, Acta of Bioengineering and Biomechanics, 2008, Vol. 10, No. 3, pp. 21-27.
  10. Department of Labor, OSHA field safety and health manual, Occupational Safety and Health Administration, 2020.
  11. Eom, J.H., An architecture of a smart safety management system to prevent accidents in workplace, Journal of Digital Contents Society, 2020, Vol. 21, No. 4, pp. 817-823. https://doi.org/10.9728/dcs.2020.21.4.817
  12. Filoneko, A. and Jo, K. H., Unattended object identification for intelligent surveillance systems using sequence of dual background difference, IEEE Transactions on Industrial Informatics, 2016, Vol. 12, No. 6, pp. 2247-2255. https://doi.org/10.1109/TII.2016.2605582
  13. Gao, Z., Zhang, H., Dong, S., Sun, S., Wang, X., Yang, G., Wu, W., Li, S., and de Albuquerque, V.H., Salient object detection in the distributed cloud edge intelligent network, IEEE Network, 2020, Vol. 34, No. 2, pp. 216-224. https://doi.org/10.1109/mnet.001.1900260
  14. Gubbi, J., Marusic, S., and Palaniswami, M., Smoke detection in video using waveletes and support vector machines, Fire Safety Journal, 2009, Vol. 44, No. 8, pp. 1110-1115. https://doi.org/10.1016/j.firesaf.2009.08.003
  15. Han, J.H., Ok, S.H., Song, K., and Jang, D.Y., CCTV monitoring system development for safety management and privacy in manufacturing site, Journal of Korean Society of Manufacturing Technology Engineers, 2017, Vol. 26, No. 3, pp. 272-277. https://doi.org/10.7735/ksmte.2017.26.3.272
  16. Hamida, A.B., Koubaa, M., Nicolas, H., and Amar, C. B., Video surveillance system based on a scalable application-oriented architecture, Multimedia Tools and Applications, 2016, Vol. 75, No. 24, pp. 17187-17213. https://doi.org/10.1007/s11042-015-2987-5
  17. International finance corporate, Environmental, Health, and Safety General Guidelines, 2007.
  18. International Finance Corporate, Envirnmental, Health, and Safety Guidelines for Thermal Power Plants, 2017.
  19. Jeon, S.Y., Park, J.H., Youn, S.B., Kim, Y.S., Lee, Y.S., and Jeon, J.H., Real-time worker safety management system using deep learning-based video analysis algorithm, The Korean Institute of Smart Media, 2020, Vol. 9, No. 3, pp. 25-30. https://doi.org/10.30693/SMJ.2020.9.3.25
  20. Kang, H.J., Established smart disaster safety management response system based on the 4th industrial revolution, Journal of Digital Contents Society, 2018, pp. 561-567. https://doi.org/10.9728/dcs.2018.19.3.561
  21. Kim, Y.C., Jung, H.W., and Bae, C.H., Prevention of human error in ship building industry, Journal of the Ergonomics Society of Korea, 2011, Vol. 30, No. 1, pp. 127-135. https://doi.org/10.5143/JESK.2011.30.1.127
  22. Kim, Y.S., Yang, S.K., Yu, K., and Kim, D.S., Flood runoff calculation using disaster monitoring CCTV system, Journal of Environmental Science International, 2014, Vol. 23, No. 4, pp. 571-584. https://doi.org/10.5322/JESI.2014.4.571
  23. Ko, K.S. and Yang, J.K., Industrial safety risk analysis using spatial analytics and data mining, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 4, pp. 147-153. https://doi.org/10.11627/jkise.2017.40.4.147
  24. Korea statistical information service, Industrial Accidents Status, https://kosis.kr/statisticsList/statisticsListIndex.do?menuId=M_01_01&vwcd=MT_ZTITLE&parmTabId=M_01_01&outLink=Y&parentId=C.1;C_14.2;#C_14.2.
  25. Lee, M.S., Park, K.O., and Lee, G.H., Management factors associated with health and safety education in Korean manufacturing companies, Korean Journal of Health Education and Promotion, 2006, Vol. 23, No. 2, pp. 121-140.
  26. Li, X., Ye, M., Liu, Y., Zhang, F., Liu, D., and Tang, S., Accurate object detection using memory-based models in surveillance scenes, Pattern Recognition, 2017, Vol. 67, pp. 73-84. https://doi.org/10.1016/j.patcog.2017.01.030
  27. Melani, A.H.A., Murad, C.A., Netto, A.C., de Souza, G.F.M., and Nabeta, S.I., Critically-based maintenance of a coal-fired power plant, Energy, 2018, Vol. 147, pp. 767-781. https://doi.org/10.1016/j.energy.2018.01.048
  28. Ministry of Employment and Labor, Analysis of Industrial Accidents, 2015.
  29. Ministry of Employment and Labor, Analysis of Industrial Accidents, 2016.
  30. Ministry of Employment and Labor, Analysis of Industrial Accidents, 2017.
  31. Ministry of Employment and Labor, Analysis of Industrial Accidents, 2018.
  32. Ministry of Employment and Labor, Analysis of Industrial Accidents, 2019.
  33. National Institute of Occupational Health, Environment, Health and Safety Issues in Coal Fired Thermal Power Plants, 2019.
  34. National Law Information Center, Rules on OSH Standards, 2021, https://www.law.go.kr/%EB%B2%95%EB%A0%B9/%EC%82%B0%EC%97%85%EC%95%88%EC%A0%84%EB%B3%B4%EA%B1%B4%EA%B8%B0%EC%A4%80%EC%97%90%EA%B4%80%ED%95%9C%EA%B7%9C%EC%B9%99
  35. Oh, H.S., Developing a quality risk assessment model for product liability law, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 3, pp. 27-37. https://doi.org/10.11627/jkise.2017.40.3.027
  36. Panchal, D. and Kumar, D., Risk analysis of compressor house unit in thermal power plant using integrated fuzzy FMEA and GRA approach, International Journal of Industrial and Systems Engineering, 2017, Vol. 25, No. 2, pp. 228-250. https://doi.org/10.1504/IJISE.2017.081519
  37. Pan, H., Su, T., Huang, X., and Wang, Z., LSTM-based soft sensor design for oxygen content of flue gas in coal-fired power plant, Transactions of the Institute of Measurement and Control, 2021, Vol. 43, No. 1, pp. 78-87. https://doi.org/10.1177/0142331220932390
  38. Panda, S.K. and Sahu, S.K., Design of IoT-based real Time video surveillance system using raspberry pi and sensor network, In Intelligent Systems, Springer Singapore, 2021, pp. 115-124.
  39. Park, J.H., Park, T.J., Lim, H.K., and Seo, E.H., Analysis of crane accidents by using a man-machine system model, Journal of the Korean Society of Safety, 2007, Vol. 22, No. 2, pp. 59-66.
  40. Ryu, J.H., Jung, T.W., Oh, H.S., Lee, S.J., and Cho. J.H., Innovation strategy for new product development process by indicative planning & QM tools, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 4, pp. 78-86.
  41. Shin, D.H. and Kim, Y.M., The utilization of big data's disaster management in Korea, The Journal of the Korea Contents Association, 2015, Vol. 15, No. 2, pp. 377-392. https://doi.org/10.5392/JKCA.2015.15.02.377
  42. Sreenu, G. and Durai, M. S., Intelligent video surveillance: a review through deep learning techniques for crowd analysis, Journal of Big Data, 2019, Vol. 6, No. 1, pp. 1-27. https://doi.org/10.1186/s40537-018-0162-3
  43. Sun, S., Akhtar, N., Song, H., Zhang, C., Li, J., and Mian, A., Benchmark data and method for real-time people counting in cluttered scenes using depth sensors, IEEE Transactions on Intelligent Transportation Systems, 2019, Vol. 20, No. 10, pp. 3599-3612. https://doi.org/10.1109/tits.2019.2911128
  44. Templer, J., Archea, J., and Chen, H. H., Study of factors associated with risk of work-related stairway falls, Journal of Safety Research, 1985, Vol. 16, No. 4, pp. 183-196. https://doi.org/10.1016/0022-4375(85)90005-2
  45. Verma, K.K., Singh, B.M., and Dixit, A., A review of supervised and unsupervised machine learning techniques for suspicious behavior recognition in intelligent surveillance system, International Journal of Information Technology, 2019, pp. 1-14.
  46. Yoon, Y.S. and Park, J.Y., A Study on improvement of safety management through statistical analysis of industrial accidents at coal-fired power plants, Journal of The Korean Institute of Plant Engineering, 2020, Vol. 25, No. 1, pp. 55-63.
  47. Yuan, F., An integrated fire detection and suppression system based on widely available video surveillance, Machine Vision and Application, 2010, Vol. 21, No. 6, pp. 941-948. https://doi.org/10.1007/s00138-010-0276-x
  48. Wahlstrom, B., Systemic thinking in support of safety management in nuclear power plants, Safety Science, 2018, Vol. 109, pp. 201-218. https://doi.org/10.1016/j.ssci.2018.06.001
  49. Wu, C.R. and Lu, B.W., Development of closed-circuit television inspection system for steam generators in nuclear power plants, Nuclear Power Plants: Innovative Technologies for Instrumentation and Control Systems, 2020, pp. 550-555.
  50. Wu, H. and Zhao, J., An intelligent vision-based approach for helmet identification for work safety, Computers in Industry, 2018, Vol. 100, pp. 267-277. https://doi.org/10.1016/j.compind.2018.03.037