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Comprehensive Analysis of IoT with Artificial Intelligence to Predictive Maintenance Optimization for Indian Shipbuilding

Author(s): PNV Srinivasa Rao* and PVY Jayasree

Publisher : FOREX Publication

Published : 23 September 2023

e-ISSN : 2347-470X

Page(s) : 800-807




PNV Srinivasa Rao*, Department of EECE, GITAM Institute of Technology, GITAM University, Visakhapatnam, India; Email: pnvsrinu@yahoo.com

PVY Jayasree, Department of EECE, GITAM Institute of Technology, GITAM University Visakhapatnam, India; Email: jpappu@gitam.edu

    [1] Gong, X., & Qiao, W. (2014). Current-based mechanical fault detection for direct-drive wind turbines via synchronous sampling and impulse detection. IEEE. Transactions on Industrial Electronics, 62(3), 1693-1702.
    [2] Bevilacqua, M., & Braglia, M. (2000). The analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering & System Safety, 70(1), 71-83. https://doi.org/10.1016/S0951-8320(00)00047-8.
    [3] Nguyen, K. A., Do, P., & Grall, A. (2015). Multi-level predictive maintenance for multi-component systems. Reliability engineering & system safety, 144, 83-94. https://doi.org/10.1016/j.ress.2015.07.017.
    [4] Wang, J., Zhang, L., Duan, L., & Gao, R. X. (2017). A new paradigm of cloud-based predictive maintenance for intelligent manufacturing. Journal of Intelligent Manufacturing, 28(5), 1125-1137. https://doi.org/10.1007/s10845-015-1066-0.
    [5] Wang, H., & Pham, H. (2006). Reliability and Optimal Maintenance of Series Systems with Imperfect Repair and Dependence. Reliability and Optimal Maintenance, 91-110. DOI: 10.1007/1-84628-325-6_5.
    [6] Mascaraque-Ramírez, C., & Para-González, L. (2022). Can the six dimensions of Marketing Promotion enhance performance in the international shipbuilding industry? Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 236(1), 245-256. https://doi.org/10.1177/14750902211003004
    [7] Jaison, M. (2021). Towards greater shipbuilding supply chain surplus in India–a review. Industrial Engineering Journal (ISSN-0970-2555), 14(1), 5-14.
    [8] Priadi, A. A. (2022). Optimalization of Smart Technologies in Improving Sustainable Maritime Transportation. In IOP Conference Series: Earth and Environmental Science (Vol. 972, No. 1, p. 012084). IOP Publishing. DOI:10.1088/1755-1315/972/1/012084.
    [9] Bermeo-Ayerbe, M. A., Ocampo-Martinez, C., & Diaz-Rozo, J. (2022). Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems. Energy, 238, 121691. https://doi.org/10.1016/j.energy.2021.121691.
    [10] Kee, K. K., Yew, S. L. B., Lim, Y. S., Ting, Y. P., & Rashidi, R. (2022). Universal cyber physical system, a prototype for predictive maintenance. Bulletin of Electrical Engineering and Informatics, 11(1), 42-49. https://doi.org/10.11591/eei.v11i1.3216.
    [11] Theodoropoulos, P., Spandonidis, C. C., & Fassois, S. (2022). Use of Convolutional Neural Networks for vessel performance optimization and safety enhancement. Ocean Engineering, 248, 110771. https://doi.org/10.1016/j.oceaneng.2022.110771.
    [12] Taşdemir, A., & Nohut, S. (2021). An overview of wire arc additive manufacturing (WAAM) in shipbuilding industry. Ships and Offshore Structures, 16(7), 797-814. https://doi.org/10.1080/17445302.2020.1786232.
    [13] Hiekata, K., & Zhao, Z. (2022). Decision Support System for Technology Deployment Considering Emergent Behaviors in the Maritime Industry. Journal of Marine Science and Engineering, 10(2), 263. https://doi.org/10.3390/jmse10020263.
    [14] Liu, C., Zhu, D., Shi, S., Teng, L., Ding, Y., & He, J. (2022). Design of Environment Monitoring System on Shipbuilding Outdoor. In Journal of Physics: Conference Series (Vol. 2168, No. 1, p. 012011). IOP Publishing. DOI 10.1088/1742-6596/2168/1/012011.
    [15] Bachtiar, A., Marimin, M., Adrianto, L., & Bura, R. (2021). Determinants of shipbuilding industry competitive factors and institutional model analysis. Decision Science Letters, 10(2), 151-162. DOI: 10.5267/j.dsl.2020.11.004.
    [16] Wahidi, S. I., Virmansyah, V. M., & Pribadi, T. W. (2021). Study on Implementation of Activity-Based Costing (ABC) System on Determination of Indirect Costs in Ship Production. Kapal: Jurnal Ilmu Pengetahuan dan Teknologi Kelautan, 18(1), 1-7. https://doi.org/10.14710/kapal.v18i1.33000.
    [17] Park, H. Y., & Lim, D. J. (2021). A design failure pre-alarming system using score-and vote-based associative classification. Expert Systems with Applications, 164, 113950. https://doi.org/10.1016/j.eswa.2020.113950.
    [18] Sahin, B., Yazir, D., Soylu, A., & Yip, T. L. (2021). Improved fuzzy AHP based game-theoretic model for shipyard selection. Ocean Engineering, 233, 109060. https://doi.org/10.1016/j.oceaneng.2021.109060.
    [19] Pribadi, T. W., & Shinoda, T. (2022). Hand Motion Analysis for Recognition of Qualified and Unqualified Welders using 9- DOF IMU Sensors and Support Vector Machine (SVM) Approach. Hand, 13(1). https://doi.org/10.14716/ijtech.v13i1.4813.
    [20] Ariany, Z., Pitana, T., & Vanany, I. (2022). Review of the Risk Assessment Methods for Shipbuilding in Indonesia. In IOP Conference Series: Earth and Environmental Science (Vol. 972, No. 1, p. 012056). IOP Publishing. DOI 10.1088/1755-1315/972/1/012056.
    [21] Caner, H. I., & Aydin, C. C. (2021). Shipyard site selection by raster calculation method and AHP in GIS environment, İskenderun, Turkey. Marine Policy, 127, 104439. https://doi.org/10.1016/j.marpol.2021.104439.
    [22] Yan, J., Meng, Y., Lu, L., & Li, L. (2017). Industrial big data in an industry 4.0 environment: Challenges, schemes, and applications for predictive maintenance. IEEE Access, 5, 23484–23491. DOI: 10.1109/ACCESS.2017.2765544.
    [23] Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 139(7), Article 071018. https://doi.org/10.1115/1.4036350.
    [24] Wu, D., Jennings, C., Terpenny, J., Gao, R., & Kumara, S. (2017). Data-driven prognostics using random forests: Prediction of tool wear. In vol. 3: Manufacturing equipment and systems. ASME, V003T04A048. https://doi.org/10.1115/MSEC2017-2679.
    [25] Wu, D., Jennings, C., Terpenny, J., Kumara, S., & Gao, R. (2017). Cloud-based parallel machine learning for prognostics and health management: A tool wear prediction case study. Journal of Manufacturing Science and Engineering, 140(4).
    [26] Wu, D., Liu, S., Zhang, L., Terpenny, J., Gao, R. X., Kurfess, T., & Guzzo, J. A. (2017). A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. Journal of Manufacturing Systems, 43, 25–34. https://doi.org/10.1016/j.jmsy.2017.02.011.
    [27] Lee, H. (2017). Framework and development of fault detection classification using IoT device and cloud environment. Journal of Manufacturing Systems, 43, 257–270. https://doi.org/10.1016/j.jmsy.2017.02.007.
    [28] Xia, M., Li, T., Liu, L., Xu, L., & de Silva, C. W. (2017). Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder. IET Science Measurement and Technology, 11(6), 687–695. https://doi.org/10.1049/iet-smt.2016.0423.
    [29] Schmidt, B., Wang, L., & Galar, D. (2017). Semantic framework for predictive maintenance in a cloud environment. Procedia CIRP, 62, 583–588. https://doi.org/10.1016/j.procir.2016.06.047.
    [30] Matyas, K., Nemeth, T., Kovacs, K., & Glawar, R. (2017). A procedural approach for realizing prescriptive maintenance planning in manufacturing industries. CIRP Annals - Manufacturing Technology, 66(1), 461–464. https://doi.org/10.1016/j.cirp.2017.04.007.
    [31] Qin, J., Liu, Y., & Grosvenor, R. (2018). Data analytics for energy consumption of digital manufacturing systems using internet of things method. In IEEE international conference on automation science and engineering (vol. 2017-Augus) (pp. 482–487). Xi’an, China: IEEE. DOI: 10.1109/COASE.2017.8256150.
    [32] Deutsch, J., & He, D. (2018). Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems Man and Cybernetics: Systems, 48(1), 11–20. DOI: 10.1109/TSMC.2017.2697842.
    [33] Ku, J. H. (2018). A study on prediction model of equipment failure through analysis of big data based on rhadoop. Wireless Personal Communication, 98(4), 3163–3176. https://doi.org/10.1007/s11277-017-4151-1.
    [34] Ayad, S., Terrissa, L. S., & Zerhouni, N. (2018). An IoT approach for a smart maintenance. In 2018 International conference on advanced systems and electric technologies (pp. 210–214). Hammamet, Tunisia: IEEE. DOI: 10.1109/ASET.2018.8379861.
    [35] Man, J., & Zhou, Q. (2018). Prediction of hard failures with stochastic degradation signals using wiener process and proportional hazards model. Computers and Industrial Engineering, 125, 480–489. https://doi.org/10.1016/j.cie.2018.09.015.
    [36] Wang, J., Liu, C., Zhu, M., Guo, P., & Hu, Y. (2018). Sensor data based system-level anomaly prediction for smart manufacturing. In 2018 IEEE international congress on big data (pp. 158–165). San Francisco, CA, USA: IEEE. DOI: 10.1109/BigDataCongress.2018.00028.
    [37] Mulrennan, K., Donovan, J., Creedon, L., Rogers, I., Lyons, J. G., & McAfee, M. (2018). A soft sensor for prediction of mechanical properties of extruded PLA sheet using an instrumented slit die and machine learning algorithms. Polymer Testing, 69, 462–469. https://doi.org/10.1016/j.polymertesting.2018.06.002.
    [38] Saez, M., Maturana, F., Barton, K., & Tilbury, D. (2018). Anomaly detection and productivity analysis for cyber-physical systems in manufacturing. In IEEE international conference on automation science and engineering (vol. 2017-Augus) (pp. 23–29). Xi’an, China. DOI: 10.1109/COASE.2017.8256070.
    [39] Kaur, K., Selway, M., Grossmann, G., Stumptner, M., & Johnston, A. (2018). Towards an open-standards based framework for achieving condition-based predictive maintenance. In Proceedings of the 8th international conference on the internet of things (pp. 16:1–16:8). New York, NY, USA: ACM. https://doi.org/10.1145/3277593.3277608.
    [40] Cho, S., May, G., Tourkogiorgis, I., Perez, R., Lazaro, O., de la Maza, B., & Kiritsis, D. (2018). A hybrid machine learning approach for predictive maintenance in smart factories of the future. In APMS 2018: Advances in production management systems. smart manufacturing for industry 4.0 (pp. 311–317). Seoul, Korea (Republic of): Springer, Cham. https://doi.org/10.1007/978-3-319-99707-0_39.
    [41] Rúbio, E. M., Dionísio, R. P., & Torres, P. M. B. (2018). Industrial IoT devices and cyber-physical production systems: Review and use case. In HELIX 2018: Innovation, engineering and entrepreneurship (vol. 505) (pp.292–298). Guimarães, Portugal. DOI: 10.1007/978-3-319-91334-6_40.
    [42] Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71–77. https://doi.org/10.1016/j.jmsy.2018.04.008
    [43] Sezer, E., Romero, D., Guedea, F., MacChi, M., & Emmanouilidis, C. (2018). An industry 4.0-enabled low cost predictive maintenance approach for SMEs. In 2018 IEEE International conference on engineering, technology and innovation (pp. 1–8). Stuttgart, Germany: IEEE. DOI: 10.1109/ICE.2018.8436307.
    [44] Nemeth, T., Ansari, F., Sihn, W., Haslhofer, B., & Schindler, A. (2018). PriMa-X: A reference model for realizing prescriptive maintenance and assessing its maturity enhanced by machine learning. Procedia CIRP, 72, 1039–1044. https://doi.org/10.1016/j.procir.2018.03.280.
    [45] Yan, H., Wan, J., Zhang, C., Tang, S., Hua, Q., & Wang, Z. (2018). Industrial big data analytics for prediction of remaining useful life based on deep learning. IEEE Access, 6, 17190–17197. DOI: 10.1109/ACCESS.2018.2809681.
    [46] Cipollini, F., Oneto, L., Coraddu, A., Murphy, A. J., & Anguita, D. (2018). Conditionbased maintenance of naval propulsion systems: Data analysis with minimal feedback. Reliability Engineering & System Safety, 177, 12–23. https://doi.org/10.1016/j.ress.2018.04.015.
    [47] He, Y., Guo, J., & Zheng, X. (2018). From surveillance to digital twin: Challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Processing Magazine, 35(5), 120–129. DOI: 10.1109/MSP.2018.2842228.
    [48] Liu, J., An, Y., Dou, R., & Ji, H. (2018). Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model. International Journal of Computational Intelligence Systems, 11(1), 846–860. https://doi.org/10.2991/ijcis.11.1.64.
    [49] Amihai, I., Pareschi, D., Gitzel, R., Subbiah, S., Kotriwala, A. M., & Sosale, G. (2018). An industrial case study using vibration data and machine learning to predict asset health. In 2018 IEEE 20th conference on business informatics (pp. 178–185). Vienna, Austria: IEEE. DOI: 10.1109/CBI.2018.00028.
    [50] Kiangala, K. S., & Wang, Z. (2018). Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts. International Journal of Advanced Manufacturing Technology, 97(9–12), 3251–3271. https://doi.org/10.1007/s00170-018-2093-8.
    [51] Lamonaca, F., Sciammarella, P. F., Scuro, C., Carni, D. L., & Olivito, R. S. (2018). Internet of things for structural health monitoring. In 2018 Workshop on metrology for industry 4.0 and IoT (pp. 95–100). Brescia, Italy: IEEE. https://doi.org/10.1016/j.conbuildmat.2021.123092.
    [52] Ardolino, M., Rapaccini, M., Saccani, N., Gaiardelli, P., Crespi, G., & Ruggeri, C. (2018). The role of digital technologies for the service transformation of industrial companies. International Journal of Productions Research, 56(6), 2116–2132. https://doi.org/10.1080/00207543.2017.1324224.
    [53] MacDermott, Á., Kendrick, P., Idowu, I., Ashall, M., & Shi, Q. (2019, June). Securing things in the healthcare internet of things. In 2019 Global IoT Summit (GIoTS) (pp. 1-6). IEEE.
    [54] der Mauer, M. A., Behrens, T., Derakhshanmanesh, M., Hansen, C., & Muderack, S. (2019). Applying sound-based analysis at porsche production: Towards predictive maintenance of production machines using deep learning and internet-of-things technology. Digitalization cases: How organizations rethink their business for the digital age, 79-97. https://doi.org/10.1007/978-3-319-95273-4_5.
    [55] Killeen, P., Ding, B., Kiringa, I., & Yeap, T. (2019). IoT-based predictive maintenance for fleet management. Procedia Computer Science, 151, 607-613. https://doi.org/10.1016/j.procs.2019.04.184
    [56] Zhang, W., Yang, D., Xu, Y., Huang, X., Zhang, J., & Gidlund, M. (2020). DeepHealth: A self-attention based method for instant intelligent predictive maintenance in industrial Internet of Things. IEEE Transactions on Industrial Informatics, 17(8), 5461-5473. DOI: 10.1109/TII.2020.3029551.
    [57] Cheng, J. C., Chen, W., Chen, K., & Wang, Q. (2020). Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction, 112, 103087. https://doi.org/10.1016/j.autcon.2020.103087.
    [58] Hansen, E. B., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 58, 362-372. https://doi.org/10.1016/j.jmsy.2020.08.009.
    [59] Chen, J., Lim, C. P., Tan, K. H., Govindan, K., & Kumar, A. (2021). Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments. Annals of Operations Research, 1-24. https://doi.org/10.1007/s10479-021-04373-w.
    [60] Jiang, Y., Dai, P., Fang, P., Zhong, R. Y., & Cao, X. (2022). Electrical-STGCN: An electrical spatio-temporal graph convolutional network for intelligent predictive maintenance. IEEE Transactions on Industrial Informatics, 18(12), 8509-8518. DOI: 10.1109/TII.2022.3143148.
    [61] Micheni, E., Machii, J., & Murumba, J. (2022, May). Internet of Things, Big Data Analytics, and Deep Learning for Sustainable Precision Agriculture. In 2022 IST-Africa Conference (IST-Africa) (pp. 1-12). IEEE. DOI: 10.23919/IST-Africa56635.2022.9845510
    [62] Wang, H., Zhang, W., Yang, D., & Xiang, Y. (2022). Deep-Learning-Enabled Predictive Maintenance in Industrial Internet of Things: Methods, Applications, and Challenges. IEEE Systems Journal. DOI: 10.1109/JSYST.2022.3193200.
    [63] Farahani, B., & Monsefi, A. K. (2023). Smart and collaborative industrial IoT: A federated learning and data space approach. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2023.01.022.
    [64] Xue, K., Yang, J., Yang, M., & Wang, D. (2023). An improved generic hybrid prognostic method for RUL prediction based on PF-LSTM learning. IEEE Transactions on Instrumentation and Measurement, 72, 1-21. DOI: 10.1109/TIM.2023.3251391.

PNV Srinivasa Rao*, PVY Jayasree (2023), Comprehensive Analysis of IoT with Artificial Intelligence to Predictive Maintenance Optimization for Indian Shipbuilding. IJEER 11(3), 800-807. DOI: 10.37391/ijeer.110325.