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http://dx.doi.org/10.11627/jkise.2021.44.3.050

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

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)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.44, no.3, 2021 , pp. 50-63 More about this Journal
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
Smart Safety Management; Safety Measurement; Power Plant; Quality Function Deployment; Failure Mode and Effect Analysis;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 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.   DOI
2 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.   DOI
3 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.   DOI
4 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.   DOI
5 Cho, J.N., A study of development for national occupational health and safety indicators, Korea Occupational Safety & Health Administration, 2015.
6 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.
7 Department of Labor, OSHA field safety and health manual, Occupational Safety and Health Administration, 2020.
8 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.   DOI
9 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.   DOI
10 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.
11 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.   DOI
12 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.
13 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.   DOI
14 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.   DOI
15 Cocca, P., Marciano, F., & Alberti, M., Video surveillance systems to enhance occupational safety: A case study, Safety Science, 2016, Vol. 26, pp. 140-148.
16 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.   DOI
17 International finance corporate, Environmental, Health, and Safety General Guidelines, 2007.
18 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.   DOI
19 International Finance Corporate, Envirnmental, Health, and Safety Guidelines for Thermal Power Plants, 2017.
20 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.   DOI
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.   DOI
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.   DOI
23 Ministry of Employment and Labor, Analysis of Industrial Accidents, 2015.
24 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.   DOI
25 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.   DOI
26 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.   DOI
27 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.
28 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.
29 Ministry of Employment and Labor, Analysis of Industrial Accidents, 2017.
30 Ministry of Employment and Labor, Analysis of Industrial Accidents, 2018.
31 National Institute of Occupational Health, Environment, Health and Safety Issues in Coal Fired Thermal Power Plants, 2019.
32 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
33 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.   DOI
34 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.
35 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.   DOI
36 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.   DOI
37 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.
38 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.   DOI
39 Ministry of Employment and Labor, Analysis of Industrial Accidents, 2016.
40 Ministry of Employment and Labor, Analysis of Industrial Accidents, 2019.
41 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.   DOI
42 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.
43 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.   DOI
44 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.   DOI
45 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.
46 Wahlstrom, B., Systemic thinking in support of safety management in nuclear power plants, Safety Science, 2018, Vol. 109, pp. 201-218.   DOI
47 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.
48 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.   DOI
49 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.   DOI
50 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.