Research Article |
Evaluation of IIOT based Pd-MaaS using CNN with Ensemble Subspace Discriminate – for Indian Ship Building in Maritime Industry
Author(s): PNV Srinivasa Rao 1* and PVY Jayasree2
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 11, Issue 1
Publisher : FOREX Publication
Published : 15 March 2023
e-ISSN : 2347-470X
Page(s) : 103-111
Abstract
Indian shipbuilding has a long history in the maritime industry dating back to the origin of civilization. India's shipbuilding sector is primarily concentrated in its coastal regions. Due to capacity constraints and decreased shipbuilding prices in emerging nations, shipbuilding activities has changed. This has created fresh opportunities for the Indian shipbuilding industry. The prospects for the Indian shipbuilding sector are improved by rising global trade and strong need for modern boats. This study investigates the use of Predictive Maintenance as a Service on the Industrial Internet of Things (IIoT-PdMaaS). Artificial intelligence (AI) in the maritime industry has numerous major benefits, including improved decision-making analysis, automation, security, route planning, and increased efficiency. So, Pd-MaaS using IIOT (Convolution neural network (CNN) with Ensemble Boosted Tree Classifier) framework was developed in this study. This research shows 88.3% accuracy of CNN output for confusion matrix implying a positive connection with our proposed model for Indian ship building industry
Keywords: Indian ship building
, Predictive maintenance as a service
, Industrial Internet of Things
, Artificial Intelligence
, Convolution neural network
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
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[1] Herterich, M. M., Uebernickel, F., & Brenner, W. (2015). The impact of cyber-physical systems on industrial services in manufacturing. Procedia Cirp, 30, 323-328. [Cross Ref]
-
[2] 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. [Cross Ref]
-
[3] Mourougane, A. (2020). Present Scenario of Ship Building Industry in Indian. Center for Development Economic, 6(06), 29-38.
-
[4] Thangam, K. M., & Sureshkumar, D. (2015). Competitiveness of Indian ship building industry. International Journal of Innovative Research & Development, 4(7), 18-25.
-
[5] Jaison, M. (2021). TOWARDS GREATER SHIPBUILDING SUPPLY CHAIN SURPLUS IN INDIA–A REVIEW. Industrial Engineering Journal (ISSN-0970-2555), 14(1), 5-14.
-
[6] Sender, J., Illgen, B., Klink, S., & Flügge, W. (2021). Integration of learning effects in the design of shipbuilding networks. Procedia CIRP, 100, 103-108. [Cross Ref]
-
[7] Shahbakhsh, M., Emad, G. R., & Cahoon, S. (2022). Industrial revolutions and transition of the maritime industry: The case of Seafarer’s role in autonomous shipping. The Asian Journal of Shipping and Logistics, 38(1), 10-18. [Cross Ref]
-
[8] Ichimura, Y., Dalaklis, D., Kitada, M., & Christodoulou, A. (2022). Shipping in the era of digitalization: Mapping the future strategic plans of major maritime commercial actors. Digital Business, 2(1), 100022. [Cross Ref]
-
[9] Theodoropoulos, P., Spandonidis, C. C., & Fassois, S. (2022). Use of Convolutional Neural Networks for vessel performance optimization and safety enhancement. Ocean Engineering, 248, 110771. [Cross Ref]
-
[10] Taşdemir, A., & Nohut, S. (2021). An overview of wire arc additive manufacturing (WAAM) in shipbuilding industry. Ships and Offshore Structures, 16(7), 797-814. [Cross Ref]
-
[11] Kiel, D., Arnold, C., & Voigt, K. I. (2017). The influence of the Industrial Internet of Things on business models of established manufacturing companies–A business level perspective. Technovation, 68, 4-19. [Cross Ref]
-
[12] Kiran, S., & Sriramoju, S. B. (2018). A Study on the Applications of IOT. Indian Journal of Public Health Research & Development, 9(11).
-
[13] Bi, Z., Da Xu, L., & Wang, C. (2014). Internet of things for enterprise systems of modern manufacturing. IEEE Transactions on industrial informatics, 10(2), 1537-1546. [Cross Ref]
-
[14] Schneider, S. (2017). The industrial internet of things (iiot) applications and taxonomy. Internet of Things and Data Analytics Handbook, 41-81. [Cross Ref]
-
[15] Kim, G. S., & Lee, Y. H. (2021). Transformation towards a smart maintenance factory: The case of a vessel maintenance depot. Machines, 9(11), 267. [Cross Ref]
-
[16] Wang, K., Yan, X., Yuan, Y., & Li, F. (2016). Real-time optimization of ship energy efficiency based on the prediction technology of working condition. Transportation Research Part D: Transport and Environment, 46, 81-93. [Cross Ref]
-
[17] 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. [Cross Ref]
-
[18] Kiangala, K. S., & Wang, Z. (2020). An effective predictive maintenance framework for conveyor motors using dual time-series imaging and convolutional neural network in an industry 4.0 environment. Ieee Access, 8, 121033-121049. [Cross Ref]
-
[19] Hafeez, T., Xu, L., & Mcardle, G. (2021). Edge intelligence for data handling and predictive maintenance in IIOT. IEEE Access, 9, 49355-49371. [Cross Ref]
-
[20] Wang, Y., Li, Y., Song, Y., & Rong, X. (2020). The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences, 10(5), 1897. [Cross Ref]
-
[21] Wu, J. (2017). Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China, 5(23), 495.
-
[22] Shoaran, M., Haghi, B. A., Taghavi, M., Farivar, M., & Emami-Neyestanak, A. (2018). Energy-efficient classification for resource-constrained biomedical applications. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 8(4), 693-707. [Cross Ref]
-
[23] Tao, W., Lai, Z. H., Leu, M. C., & Yin, Z. (2018). Worker activity recognition in smart manufacturing using IMU and sEMG signals with convolutional neural networks. Procedia Manufacturing, 26, 1159-1166. [Cross Ref]
-
[24] Yuan, Y., Ma, G., Cheng, C., Zhou, B., Zhao, H., Zhang, H. T., & Ding, H. (2018). Artificial intelligent diagnosis and monitoring in manufacturing. arXiv preprint arXiv:1901.02057.
-
[25] 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. [Cross Ref]
-
[26] Li, L., Ota, K., & Dong, M. (2018). Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics, 14(10), 4665-4673. [Cross Ref]
-
[27] Jiang, L., Ge, Z., & Song, Z. (2017). Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model. Chemometrics and Intelligent Laboratory Systems, 168, 72-83. [Cross Ref]
-
[28] Zhang, W., Guo, W., Liu, X., Liu, Y., Zhou, J., Li, B., ... & Yang, S. (2018). LSTM-based analysis of industrial IoT equipment. IEEE Access, 6, 23551-23560. [Cross Ref]
-
[29] Krishnaveni, P., & Sutha, J. (2020). Novel deep learning framework for broadcasting abnormal events obtained from surveillance applications. Journal of Ambient Intelligence and Humanized Computing, 1-15. [Cross Ref]
-
[30] Bharathi, S., & Venkatesan, P. (2022). Enhanced Classification of Faults of Photovoltaic Module Through Generative Adversarial Network. IJEER, 10(3), 579-584. [Cross Ref]
-
[31] Femy, P. H., & Jayakumar, J. (2021). A Review on the Feasibility of Deployment of Renewable Energy Sources for Electric Vehicles under Smart Grid Environment. IJEER, 9(3), 57-65. [Cross Ref]
-
[32] Kulkarni, S., & Thosar, A. (2022). Performance Analysis of Fault Tolerant Operation of PMSM using Direct Torque Control and Fuzzy Logic Control. IJEER, 10(2), 297-307. [Cross Ref]
-
[33] Ramanna, D., & Ganesan, V. (2022). Low-Power VLSI Implementation of Novel Hybrid Adaptive Variable-Rate and Recursive Systematic Convolutional Encoder for Resource Constrained Wireless Communication Systems. IJEER, 10(3), 523-528.Fröhlich, B. and Plate, J. 2013. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. [Cross Ref]