Development of an IoT-Based Ship Engine Performance Monitoring System to Enhance Operational Efficiency

Imam  Sutrisno; Fahmi  Umasangadji; Ardiansyah; Nafi  Almuzani; Achmad  Bashori; Urip  Mudjiono; Projek  Priyonggo; Endang Pudji  Purwanti

doi:10.55927/fjcis.v4i1.14134

Authors

Imam Sutrisno Politeknik Perkapalan Negeri Surabaya
Fahmi Umasangadji Sekolah Tinggi Ilmu Pelayaran
Ardiansyah Sekolah Tinggi Ilmu Pelayaran
Nafi Almuzani Sekolah Tinggi Ilmu Pelayaran
Achmad Bashori Sekolah Tinggi Ilmu Pelayaran
Urip Mudjiono Politeknik Perkapalan Negeri Surabaya
Projek Priyonggo Politeknik Perkapalan Negeri Surabaya
Endang Pudji Purwanti Politeknik Perkapalan Negeri Surabaya

DOI:

https://doi.org/10.55927/fjcis.v4i1.14134

Keywords:

IoT, Ship Engine Monitoring, Operational Efficiency, Real-Time Data, Marine Engineering

Abstract

This research aims to develop an IoT-based ship engine performance monitoring system to enhance operational efficiency in marine vessels. The study introduces a real-time monitoring prototype integrating sensors, microcontrollers, and cloud-based data visualization. The variables observed include engine temperature, RPM, and fuel consumption. The methodology follows a design and implementation approach, tested on a diesel-powered training ship over a two-week observation period. Data were collected continuously and analyzed to detect performance anomalies and efficiency trends. Results show that the system improves situational awareness and enables proactive maintenance decisions. The developed system has implications for reducing fuel costs, extending engine life, and supporting digital transformation in marine engineering operations

Downloads

Download data is not yet available.

References

Anfasa, I. and Sutrisno, I. (2021). RANCANG BANGUN INTEGRASI SCADA PADA SISTEM CRUSHING DAN BARGE LOADING CONVEYOR. Jurnal Conference on Automation Engineering and Its Application.

Ahmed, M. U., & Khan, S. (2023). Digital twins and IoT for smart ship engine diagnostics: A review and future directions. Sensors, 23(3), 1129. https://doi.org/10.3390/s23031129

Tan, B., Chen, L., & Zhou, X. (2023). Anomaly detection in ship propulsion systems using AI-based IoT platforms. Ocean Engineering, 275, 113240. https://doi.org/10.1016/j.oceaneng.2023.113240

Liu, X., Zhang, W., & Huang, Y. (2022). Predictive maintenance of marine engines using sensor fusion and deep learning techniques. IEEE Access, 10, 77654–77666. https://doi.org/10.1109/ACCESS.2022.3189207

Lee, J., Park, H., & Kim, Y. (2021). Real-time IoT architecture for marine engine monitoring using MQTT and edge computing. Journal of Marine Science and Engineering, 9(5), 487. https://doi.org/10.3390/jmse9050487

Danis B, Agus K, Projek P, Mohammad B, and Sutrisno, I. (2019). Ball Direction Prediction for Wheeled Soccer Robot Goalkeeper Using Trigonometry Technique. Applied Technology and Computing Science Journal.

Budianto, I. et al (2020). Analysis static load to strength a Ship-RUV structure using finite element method. IOP Conf. Series: Materials Science and Engineering 1175 (2021) 012017.

Hakim, A. S., & Munaf, R. (2019). Analysis of Speedboat Accidents in Waters of Tanjung Benoa, Bali, Indonesia. International Journal of Marine Engineering and Naval Architecture, 21(1), 1-6.

Hananur, R. N. and Sutrisno, I. (2018). Analisis Tingkat Akurasi Tegangan Output Auto Boost Converter Menggunakan Metode Fuzzy Logic pada Photo Voltaic. Seminar MASTER PPNS.

Hasugian, S., Rahmawati, M. and Sutrisno, I. (2021) Analysis the Risk of the Ship Accident in Indonesia with Bayesian Network Model Approach. Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 2, Pages. 3341 - 3356

Hayati, N. F., & Munaf, R. (2018). Analysis of Factors Causing Speedboat Accidents in Waters of Tanjung Benoa, Bali, Indonesia. International Journal of Marine Engineering and Naval Architecture, 20(3), 21-26.

Iskandar, Dewa, P., and Sutrisno, I. (2022). Prototype of Bridge Navigational Watch Alarm System Equipped Obstacle Warning System Based on Image Processing and Real-Time Tracking. International journal of Marine Engineering and Research. Volume 7. No 1.

Jami’in, M. A., Sutrisno, I., and Hu, J. (2015). The State-Dynamic-Error-Based Switching Control under Quasi-ARX Neural Network Model. AROB 20th B-Con Plaza, Beppu, Japan

Jami’in, M. A., Sutrisno, I., and Hu, J. (2014). Nonlinear Adaptive Control for Wind Energy Conversion Systems Based on Quasi-ARX Neural Network Model. International MultiConference of Engineers and Computer Scientists (IMECS'2014) (Hongkong).

Khumaidi, A. et al (2018). Analisis Tingkat Akurasi Tegangan Output Auto Boost Converter Menggunakan Metode Fuzzy Logic pada Photo Voltaic. Seminar MASTER PPNS.

Kurniawan, A., & Munaf, R. (2017). Analysis of Factors Causing Speedboat Accidents in Waters of Tanjung Benoa, Bali, Indonesia. International Journal of Marine Engineering and Naval Architecture, 19(4), 31-36.

Mohammad B, Sutrisno, I., Budianto, Santosa, A. W. B., and Nofandi, F (2020). Vibration Analysis of Ship-RUV Structure in Operational Conditions. IOP Conf. Series: Earth and Environmental Science 519 012045

Munaf, R., & Handayani, H. F. (2016). Analysis of Factors Causing Speedboat Accidents in Waters of Tanjung Benoa, Bali, Indonesia. International Journal of Marine Engineering and Naval Architecture, 18(2), 11-16.

Nofandi, F., Devandra, RH., Hasugian, S., Sutrisno, I., Setiawan. E. Design floating robot of shallots irrigation with GPS based and using the waypoint navigation method. IOP Conference Series: Materials Science and Engineering

Rifai, M., et al (2021). Dynamic time distribution system monitoring on traffic light using image processing and convolutional neural network method. IOP Conf. Series: Materials Science and Engineering 1175.

Santosa, AWB., Hardianti, A., Hasugian, S., Sutrisno, I., Khumaidi, A. (2021). Safe distance reminder system on ship against port for the standing process using image processing. IOP Conference Series: Materials Science and Engineering.

Sutrisno, I. (2020). Vibration Analysis of Ship-RUV Structure in Operational. International Conference Earth Science & Energy

Sutrisno, M. and Muhammad F, dkk, (2019). Implementation of Backpropagation Neural Network and Extreme Learning Machine of pH Neutralization Prototype. IOP Conf. Series: Journal of Physics: Conf. Series 1196 012048

Sutrisno, I. (2009). Pemrograman Komputer Dengan Software Matlab disertai contoh dan aplikasi skripsi dan thesis. ITS Press.

Sutrisno, I., et al (2013). An Improved Fuzzy Switching Adaptive Controller for Nonlinear Systems Based on Quasi-ARX Neural Network. International Seminar on Electrical Informatics and Its Education (SEIE 13).

Sutrisno, I. et al (2013). Implementation of Lyapunov Learning Algorithm for Fuzzy Switching Adaptive Controller Modeled Under Quasi-ARX Neural Network. Inter. Conference on Measurement, Information and Control

Sutrisno, I., et al (2014). Nonlinear Model-Predictive Control Based on Quasi-ARX Radial-Basis Function-Neural-Network. 2014 8th Asia Modelling Symposium.

Sutrisno, I., Che, C. and Hu, J. (2014). Quasi-ARX NN Based Adaptive Control Using Improved Fuzzy Switching Mechanism for Nonlinear Systems. AROB 19th B-Con Plaza, Beppu, Japan.

Sutrisno, I. and Jami’in, M. A. (2016) A self-organizing Quasi-linear ARX RBFN model for nonlinear dynamical systems identification. SICE Journal of Control, Measurement, and System Integration.