Defect Analysis to Improve Quality in Traditional Shipbuilding Processes
https://doi.org/10.56225/ijgoia.v1i3.64
Keywords:
shipbuilding, defect, reliability-centered, maintenance, industrial designAbstract
As one of the livelihoods of the Indonesian people, the sea has many benefits, such as fish, marine plant cultivation, and others. Most Indonesian live in the coastal area and work as fishermen. Its existence and role are very important in coastal development, especially fisheries. This study aims to investigate logic tree analysis to solve defects in traditional shipbuilding. It is envisaged that such implementation shall improve the quality of ships produced. Traditional ships are currently experiencing a significant demand proportionally to the increase in the number of fishermen utilizing traditional ships. However, shipbuilders experienced many obstacles during the manufacturing process. Therefore, the quality of the ships produced was different from the ship owners' standards or demands. For solving this problem, initial identification processes should be conducted to determine any potential defects that may affect the quality of the ships. Defects that pose a high risk to the quality of the ship were resolved using a logic tree analysis to obtain recommendations for improvements. The result shows that four risks of damage have been identified that affect the quality of the traditional ships produced: the selection of the quality of raw materials, imperfect wooden joints, easily corroded steel bolts, and poor installation of additional equipment.
Downloads
References
Abdelgawad, M., & Fayek, A. R. (2012). Comprehensive hybrid framework for risk analysis in the construction industry using combined failure mode and effect analysis, fault trees, event trees, and fuzzy logic. Journal of Construction Engineering and Management, 138(5), 642–651. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000471
Akyuz, E., Arslan, O., & Turan, O. (2020). Application of fuzzy logic to fault tree and event tree analysis of the risk for cargo liquefaction on board ship. Applied Ocean Research, 101, 102238. https://doi.org/10.1016/j.apor.2020.102238
Alencar, M. H., & de Almeida, A. T. (2015). A multicriteria decision model for assessment of failure consequences in the RCM approach. Mathematical Problems in Engineering, 2015, 1–10. https://doi.org/10.1155/2015/729865
Arabian-Hoseynabadi, H., Oraee, H., & Tavner, P. J. (2010). Failure modes and effects analysis (FMEA) for wind turbines. International Journal of Electrical Power & Energy Systems, 32(7), 817–824. https://doi.org/10.1016/j.ijepes.2010.01.019
Bommer, J. J., & Scherbaum, F. (2008). The use and misuse of logic trees in probabilistic seismic hazard analysis. Earthquake Spectra, 24(4), 997–1009. https://doi.org/10.1193/1.297775
Gupta, G., Mishra, R. P., & Singhvi, P. (2016). An application of reliability centered maintenance using RPN mean and range on conventional lathe machine. International Journal of Reliability, Quality and Safety Engineering, 23(6), 1–10. https://doi.org/10.1142/S0218539316400106
Kim, K. O., & Zuo, M. J. (2018). General model for the risk priority number in failure mode and effects analysis. Reliability Engineering & System Safety, 169, 321–329. https://doi.org/10.1016/j.ress.2017.09.010
Pamungkas, I, & Dirhamsyah, M. (2019). Monte Carlo simulation for predicting the reliability of a boiler in the Nagan Raya steam power plant. IOP Conference Series: Materials Science and Engineering, 523(1), 12071.
Pamungkas, I, Irawan, H. T., & Saputra, A. (2020). Risk and reliability analysis on critical components of boiler in steam power plant. IOP Conference Series: Materials Science and Engineering, 1003(1), 12048.
Pamungkas, Iing, & Irawan, H. T. (2020). Strategi Pengurangan Risiko Kerusakan Pada Komponen Kritis Boiler di Industri Pembangkit Listrik. Jurnal Optimalisasi, 6(1), 86–95. https://doi.org/10.35308/JOPT.V6I1.2197
Pamungkas, Iing, Irawan, H. T., Arkanullah, L., Dirhamsyah, M., & Iqbal, M. (2019). Penentuan Tingkat Risiko pada Proses Produksi Garam Tradisional di Desa Ie Leubeu Kabupaten Pidie. Jurnal Optimalisasi, 5(2), 107–120. https://doi.org/10.35308/JOPT.V5I2.1307
Porter, K., Field, E., & Milner, K. (2017). Trimming a hazard logic tree with a new model-order-reduction technique. Earthquake Spectra, 33(3), 857–874. https://doi.org/10.1193/092616eqs158
Rabinowitz, N., Steinberg, D. M., & Leonard, G. (1998). Logic trees, sensitivity analyses, and data reduction in probabilistic seismic hazard assessment. Earthquake Spectra, 14(1), 189–201. https://doi.org/10.1193/1.1585995
Ramzali, N., Lavasani, M. R. M., & Ghodousi, J. (2015). Safety barriers analysis of offshore drilling system by employing fuzzy event tree analysis. Safety Science, 78, 49–59. https://doi.org/10.1016/j.ssci.2015.04.004
Rastayesh, S., Bahrebar, S., Blaabjerg, F., Zhou, D., Wang, H., & Dalsgaard Sørensen, J. (2019). A system engineering approach using FMEA and Bayesian network for risk analysis—a case study. Sustainability, 12(1), 77. https://doi.org/10.3390/su12010077
Rhee, S. J., & Ishii, K. (2003). Using cost based FMEA to enhance reliability and serviceability. Advanced Engineering Informatics, 17(3–4), 179–188. https://doi.org/10.1016/j.aei.2004.07.002
Satake, K., Okal, E. A., & Borrero, J. C. (2007). Tsunami and its hazards in the Indian and Pacific oceans. Springer Science & Business Media.
Song, K.-T., Kim, M.-H., Baek, Y.-G., Shin, K.-Y., & Lee, K.-S. (2008). The Development of Logic of LTA (Logic Tree Analysis) for an Effective RCM Application of Rolling stock. Proceedings of the KSR Conference, 1562–1569.
Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. Quality Press.
Tang, Y., Liu, Q., Jing, J., Yang, Y., & Zou, Z. (2017). A framework for identification of maintenance significant items in reliability centered maintenance. Energy, 118, 1295–1303. https://doi.org/10.1016/j.energy.2016.11.011
Tang, Y., Zou, Z., Jing, J., Zhang, Z., & Xie, C. (2015). A framework for making maintenance decisions for oil and gas drilling and production equipment. Journal of Natural Gas Science and Engineering, 26, 1050–1058. https://doi.org/10.1016/j.jngse.2015.07.038
Waghen, K., & Ouali, M.-S. (2019). Interpretable logic tree analysis: A data-driven fault tree methodology for causality analysis. Expert Systems with Applications, 136, 376–391. https://doi.org/10.1016/j.eswa.2019.06.042
Waghen, K., & Ouali, M.-S. (2021). Multi-level interpretable logic tree analysis: A data-driven approach for hierarchical causality analysis. Expert Systems with Applications, 178, 115035. https://doi.org/10.1016/j.eswa.2021.115035
Yucesan, M., Gul, M., & Celik, E. (2021). A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method. Complex & Intelligent Systems, 7(3), 1547–1564. https://doi.org/10.1007/s40747-021-00279-z
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
