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http://dx.doi.org/10.1016/j.ijnaoe.2019.03.002

A dynamic human reliability assessment approach for manned submersibles using PMV-CREAM  

Zhang, Shuai (Shaanxi Engineering Laboratory for Industrial Design, Northwest Polytechnical University (NWPU))
He, Weiping (Shaanxi Engineering Laboratory for Industrial Design, Northwest Polytechnical University (NWPU))
Chen, Dengkai (Shaanxi Engineering Laboratory for Industrial Design, Northwest Polytechnical University (NWPU))
Chu, Jianjie (Shaanxi Engineering Laboratory for Industrial Design, Northwest Polytechnical University (NWPU))
Fan, Hao (Shaanxi Engineering Laboratory for Industrial Design, Northwest Polytechnical University (NWPU))
Publication Information
International Journal of Naval Architecture and Ocean Engineering / v.11, no.2, 2019 , pp. 782-795 More about this Journal
Abstract
Safety is always acritical focus of exploration of ocean resources, and it is well recognized that human factor is one of the major causes of accidents and breakdowns. Our research developed a dynamic human reliability assessment approach, Predicted Mean Vote-Cognitive Reliability and Error Analysis Method (PMV-CREAM), that is applicable to monitoring the cognitive reliability of oceanauts during deep-sea missions. Taking into account the difficult and variable operating environment of manned submersibles, this paper analyzed the cognitive actions of oceanauts during the various procedures required by deep-sea missions, and calculated the PMV index using human factors and dynamic environmental data. The Cognitive Failure Probabilities (CFP) were calculated using the extended CREAM approach. Finally, the CFP were corrected using the PMV index. This PMV-CREAM hybrid model can be utilized to avoid human error in deep-sea research, thereby preventing injury and loss of life during undersea work. This paper verified the method with "Jiaolong" manned submersible 7,000 m dive test. The"Jiaolong" oceanauts CR(Corrected CFP) is dynamic from 3.0615E-3 to 4.2948E-3, the CR caused by the environment is 1.2333E-3. The result shown the PMV-CREAM method could describe the dynamic human reliability of manned submersible caused by thermal environment.
Keywords
Manned submersible; Deep sea exploration; Predicted Mean Vote (PMV); Human Reliability Analysis (HRA); Human factors; Cognitive behavior;
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  • Reference
1 Pyy, P., 2000. An approach for assessing human decision reliability. Reliab. Eng. Syst. Saf. 68, 17-28. https://doi.org/10.1016/S0951-8320(99)00078-2.   DOI
2 Randolph Thomas, H., Yiakoumis, I., 1987. Factor model of construction productivity. J. Constr. Eng. Manag. 113, 623-639. https://doi.org/10.1061/(ASCE)0733-9364(1987)113:4(623).   DOI
3 Sagalevitch, 1998. Experience of the use of manned submersibles in PP shirshov institute of oceanology of Russian academy of sciences. In: Underw. Technol. 1998. Proc. 1998 Int. Symp., pp. 403-407. https://doi.org/10.1109/UT.1998.670137.
4 Sarhan, A.M., Tadj, L., Al-khedhairi, A., Mustafa, A., 2008. Equivalence factors of a parallel-series system. Appl. Sci. 10, 219-230.   DOI
5 Schellen, L., Loomans, M.G.L.C., de Wit, M.H., Olesen, B.W., Lichtenbelt, W.D. van M., 2012. The influence of local effects on thermal sensation under non-uniform environmental conditions - gender differences in thermophysiology, thermal comfort and productivity during convective and radiant cooling. Physiol. Behav. 107, 252-261. https://doi.org/10.1016/j.physbeh.2012.07.008.   DOI
6 Sulaiman, A.H. Saharuddin, Kader, A.S.A., 2012. Human reliability analysis (HRA) emanating from use of technology for ships navigating within coastal area. Afr. J. Bus. Manag. 6, 3602. https://doi.org/10.5897/AJBM10.1636.
7 Sun, Y.P., Zhu, N., Tian, Z., 2012. Measurement and evaluation for productivity in extreme hot environment. Appl. Mech. Mater. 209-211, 1496-1499. https://doi.org/10.4028/www.scientific.net/AMM.209-211.1496.   DOI
8 Swain, A.D., 1963. Method for Performing a Human-Factors Reliability Analysis.
9 Taylor, L., Lawson, T., 2009. Project deepsearch: an innovative solution for accessing the oceans. Mar. Technol. Soc. J. 43, 169-177. https://doi.org/10.4031/MTSJ.43.5.28.   DOI
10 Thompson, C.M., Cooper, S.E., Kolaczkowski, A.M., Bley, D.C., Forester, J.A., Wreathall, J., 1997. The application of ATHEANA: a technique for human error analysis. In: Hum. Factors Power Plants, 1997. Glob. Perspect. Hum. Factors Power Gener. Proc. 1997 IEEE Sixth Conf., pp. 9-13. https://doi.org/10.1109/HFPP.1997.624860.
11 Tingle, C., 2009. Submarine accidents. Prof. Saf. 54, 31-39.
12 Ung, S.-T., 2015. A weighted CREAM model for maritime human reliability analysis. Saf. Sci. 72, 144-152. https://doi.org/10.1016/j.ssci.2014.08.012.   DOI
13 Van Hoof, J., 2008. Forty years of Fanger's model of thermal comfort: comfort for all? Indoor Air 18, 182-201. https://doi.org/10.1111/j.1600-0668.2007.00516.x.   DOI
14 Walden, B.B., Brown, R.S., 2004. A replacement for the Alvin submersible. Mar. Technol. Soc. J. 38, 85-91. https://doi.org/10.4031/002533204787522721.   DOI
15 Webb, R.D., Lamoureux, T.M., 2003. Human Reliability and Ship Stability.
16 Yalaoui, A., Chu, C., Chatelet, E., 2005. Reliability allocation problem in a seriesparallel system. Reliab. Eng. Syst. Saf. 90, 55-61. https://doi.org/10.1016/j.ress.2004.10.007.   DOI
17 Yang, Z.L., Bonsall, S., Wall, A., Wang, J., Usman, M., 2013. A modified CREAM to human reliability quantification in marine engineering. Ocean Eng. 58, 293-303. https://doi.org/10.1016/j.oceaneng.2012.11.003.   DOI
18 Zhang, L., Lu, J., Ai, Y., 2014. Analysis and prediction on combination patterns of human factors for maritime accidents. In: CICTP 2014 Safe, Smart, Sustain. Multimodal Transp. Syst., pp. 2313-2322. https://doi.org/10.1061/9780784413623.222
19 ASHRAE, 2004. Thermal environmental conditions for human occupancy, ASHRAE standard 55. Am. Soc. Heating, Refrig. Air-Conditioning Eng. 55 (2004). Atlanta, GA.
20 Zhou, Q., Wong, Y.D., Xu, H., Van Thai, V., Loh, H.S., Yuen, K.F., 2017. An enhanced CREAM with stakeholder-graded protocols for tanker shipping safety application. Saf. Sci. 95, 140-147. https://doi.org/10.1016/j.ssci.2017.02.01.   DOI
21 Williams, J.C., 1988. A data-based method for assessing and reducing human error to improve operational performance. In: Hum. Factors Power Plants, 1988., Conf. Rec. 1988 IEEE Fourth Conf., pp. 436-450. https://doi.org/10.1109/HFPP.1988.27540.
22 CEN, 2007. prEN 15251: Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings-Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics. Eur. Comm. Stand.
23 Akimoto, T., ichi Tanabe, S., Yanai, T., Sasaki, M., 2010. Thermal comfort and productivity - evaluation of workplace environment in a task conditioned office. Build. Environ. 45, 45-50. https://doi.org/10.1016/j.buildenv.2009.06.022.   DOI
24 Ashley, S., 1993. Voyage to the bottom of the sea. Mech. Eng. 115, 52.
25 Blackman, H.S., Gertman, D.I., Boring, R.L., 2008. Human error quantification using performance shaping factors in the SPAR-H method. In: Proc. Hum. Factors Ergon. Soc. Annu. Meet., pp. 1733-1737. https://doi.org/10.1177/154193120805202109.
26 Boring, R.L., 2007. Dynamic human reliability analysis: benefits and challenges of simulating human performance. Proc. Eur. Saf. Reliab. Conf. (ESREL 2007) 1043-1050.
27 Boulegue, J., Iiyama, J.T., Charlou, J.-L., Jedwab, J., 1987. Nankai trough, Japan trench and kuril trench: geochemistry of fluids sampled by submersible "Nautile". Earth Planet. Sci. Lett. 83, 363-375. https://doi.org/10.1016/0012-821X(87)90078-1.   DOI
28 Chen, Z., Zhang, W., Dong, D., Lu, K., 2017. An extended CREAM by modified algorithm to human reliability quantification in marine engineering. In: Proc. - Annu. Reliab. Maintainab. Symp.. https://doi.org/10.1109/RAM.2017.7889696.
29 Embrey, D.E., Humphreys, P., Rosa, E.A., Kirwan, B., Rea, K., 1984. SLIM-MAUD: an Approach to Assessing Human Error Probabilities Using Structured Expert Judgment. Volume II. Detailed Analysis of the Technical Issues.
30 Fanger, P.O., 1970. Thermal comfort. Analysis and applications in environmental engineering. In: Therm. Comf. Anal. Appl. Environ. Eng., Danish Technical Press, Copenhagen, p. 244.
31 GB/T, 2012. Evaluation Standard for Indoor Thermal Environment in Civil Buildings, GB/T 50785(in Chinese). Minist. Hous. Urban-Rural Dev, People's Repub. China, Beijing.
32 Hollnagel, E., 1998. Cognitive Reliability and Error Analysis Method (CREAM). https://doi.org/10.1016/B978-008042848-2/50001-4.
33 Geng, Y., Ji, W., Lin, B., Zhu, Y., 2017. The impact of thermal environment on occupant IEQ perception and productivity. Build. Environ. 121, 158-167. https://doi.org/10.1016/j.buildenv.2017.05.022.   DOI
34 Hancher, D.E., Abd-Elkhalek, H.A., 1998. Effect of hot weather on construction labor productivity and costs. Cost Eng. 40, 32-36.
35 Hardy, K., Cameron, J., Herbst, L., Bulman, T., Pausch, S., 2013. Hadal landers: the DEEPSEA CHALLENGE ocean trench free vehicles. In: Ocean. Diego, vol 2013, pp. 1-10. https://doi.org/10.23919/OCEANS.2013.6741368.
36 ISO, 2005. 7730 Ergonomics of the Thermal Environment, Anal. Determ. Interpret. Therm. Comf. Using Calc. PMV PPD Indices Local Therm. Comf. Criteria. International Organization for Standardization, Geneva, Switzerland.
37 Iwai, Y., Nakanishi, T., Takahashi, K., 1990. Sea trials and supporting technologies of manned submersible Shinkai 6500. In: Interv. Sous-Marine ISM 90, Toulon (France), 3-5 Dec 1990.
38 Koehn, E., Brown, G., 1985. Climatic effects on construction. J. Constr. Eng. Manag. 111, 129-137. https://doi.org/10.1061/(ASCE)0733-9364(1985)111:2(129).   DOI
39 Kosonen, R., Tan, F., 2004. Assessment of productivity loss in air-conditioned buildings using PMV index. Energy Build. 36, 987-993. https://doi.org/10.1016/j.enbuild.2004.06.021.   DOI
40 Kohnen, W., 2009. Human exploration of the deep seas: fifty years and the inspiration continues. Mar. Technol. Soc. J. 43, 42-62. https://doi.org/10.4031/MTSJ.43.5.30.   DOI
41 Lan, L., Wargocki, P., Lian, Z., 2011. Quantitative measurement of productivity loss due to thermal discomfort. Energy Build. 43, 1057-1062. https://doi.org/10.1016/j.enbuild.2010.09.001.   DOI
42 Liu, F., Cui, W.C., Li, X.Y., 2010. China's first deep manned submersible. JIAOLONG, Sci. China Earth Sci. 53, 1407-1410. https://doi.org/10.1007/s11430-010-4100-2.   DOI
43 Luo, M., de Dear, R., Ji, W., Bin, C., Lin, B., Ouyang, Q., Zhu, Y., 2016. The dynamics of thermal comfort expectations: the problem, challenge and implication. Build. Environ. 95, 322-329. https://doi.org/10.1016/j.buildenv.2015.07.015.   DOI
44 Mohamed, S., Srinavin, K., 2005. Forecasting labor productivity changes in construction using the PMV index. Int. J. Ind. Ergon. 35, 345-351. https://doi.org/10.1016/j.ergon.2004.09.008.   DOI
45 Ole Fanger, P., Toftum, J., 2002. Extension of the PMV model to non-air-conditioned buildings in warm climates. Energy Build. 34, 533-536. https://doi.org/10.1016/S0378-7788(02)00003-8.   DOI
46 Pourzanjani, M., Zheng, P., 2001. Human reliability assessment for ship encounters. In: Saf. Reliab., pp. 21-30. https://doi.org/10.1080/09617353.2001.11690712.
47 Jin, L., Zhang, Y., Zhang, Z., 2017. Human responses to high humidity in elevated temperatures for people in hot-humid climates. Build. Environ. 114, 257-266. https://doi.org/10.1016/j.buildenv.2016.12.028.   DOI