Power Station Engine Failure Early Warning System Using Thermal Camera
DOI:
10.29303/jppipa.v9i8.4598Published:
2023-08-25Issue:
Vol. 9 No. 8 (2023): AugustKeywords:
Anomaly detection, Computer vision, Machine learning, Power station engine, Thermal cameraResearch Articles
Downloads
How to Cite
Downloads
Metrics
Abstract
Power station engines are critical infrastructure components that require constant monitoring to prevent failures and ensure an uninterrupted power supply. This paper proposes a failure early warning system based on a thermal camera using a computer vision approach. The system uses a thermal camera to generate thermal images in a video format, which is then processed by an automated fire detection engine and temperature detection engine. The results of these two subsystems are then used as input for an anomaly detection engine, which predicts the likelihood of engine failure. Based on the results of the experiments, it can be concluded that the YOLOv7 model outperforms Faster R-CNN in detecting fires, achieving a higher mAP score on the one-class dataset. The proposed temperature and anomaly detection system also accurately detected temperature levels and anomalies in thermal images. Furthermore, in the failure time prediction experiment, the Holt-Winters additive method with additive errors, additive trend, and additive seasonality model was identified as the best fit among the models evaluated. In contrast, the Decision Tree model showed good performance and a short training time, making it a good choice for applications where training time is critical. These results highlight the importance of selecting the most suitable method for a given application. Moreover, it demonstrates the effectiveness of different models and approaches for engine failure early warning systems in a power station using a thermal camera.
References
Adnan, M., AlSaeed, D. H., Al-Baity, H. H., & Rehman, A. (2021). Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model. Complexity, 2021, 1–21. https://doi.org/10.1155/2021/5519769
Almazroa, H., & Alotaibi, W. (2023). Teaching 21st Century Skills: Understanding the Depth and Width of the Challenges to Shape Proactive Teacher Education Programmes. Sustainability, 15(9), 7365. https://doi.org/10.3390/su15097365
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., SantamarÃa, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8
Andoga, R., FÅ‘zÅ‘, L., Schrötter, M., ÄŒeÅ¡koviÄ, M., Szabo, S., Bréda, R., & Schreiner, M. (2019). Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines. Applied Sciences, 9(11), 2253. https://doi.org/10.3390/app9112253
Bhadoriya, A. S., Vegamoor, V., & Rathinam, S. (2022). Vehicle Detection and Tracking Using Thermal Cameras in Adverse Visibility Conditions. Sensors, 22(12), 4567. https://doi.org/10.3390/s22124567
Binta Islam, S., Valles, D., Hibbitts, T. J., Ryberg, W. A., Walkup, D. K., & Forstner, M. R. J. (2023). Animal Species Recognition with Deep Convolutional Neural Networks from Ecological Camera Trap Images. Animals, 13(9), 1526. https://doi.org/10.3390/ani13091526
Chen, J., Li, D., & Xu, J. (2022). Sustainable Development of EFL Teachers’ Technological Pedagogical Content Knowledge (TPACK) Situated in Multiple Learning Activity Systems. Sustainability, 14(14), 8934. https://doi.org/10.3390/su14148934
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So What if ChatGPT Wrote it?†Multidisciplinary Perspectives on Opportunities, Challenges and Implications of Generative Conversational AI for Research, Practice and Policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Eash-Gates, P., Klemun, M. M., Kavlak, G., McNerney, J., Buongiorno, J., & Trancik, J. E. (2020). Sources of Cost Overrun in Nuclear Power Plant Construction Call for a New Approach to Engineering Design. Joule, 4(11), 2348–2373. https://doi.org/10.1016/j.joule.2020.10.001
Filippini, C., Perpetuini, D., Cardone, D., Chiarelli, A. M., & Merla, A. (2020). Thermal Infrared Imaging-Based Affective Computing and Its Application to Facilitate Human Robot Interaction: A Review. Applied Sciences, 10(8), 2924. https://doi.org/10.3390/app10082924
Hassani, S., & Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23(4), 2204. https://doi.org/10.3390/s23042204
Hodges, C., Bennamoun, M., & Boussaid, F. (2021). Quantitative Performance Evaluation of Object Detectors in Hazy Environments. Pattern Recognition Letters, 152, 150–157. https://doi.org/10.1016/j.patrec.2021.10.001
Hussain, M. (2023). YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. Machines, 11(7), 677. https://doi.org/10.3390/machines11070677
Iwendi, C., Khan, S., Anajemba, J. H., Mittal, M., Alenezi, M., & Alazab, M. (2020). The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems. Sensors, 20(9), 2559. https://doi.org/10.3390/s20092559
Majchrzak, D., Michalski, K., & Reginia-Zacharski, J. (2021). Readiness of the Polish Crisis Management System to Respond to Long-Term, Large-Scale Power Shortages and Failures (Blackouts). Energies, 14(24), 8286. https://doi.org/10.3390/en14248286
Nobre, J., Pires, E. J. S., & Reis, A. (2023). Anomaly Detection in Microservice-Based Systems. Applied Sciences, 13(13), 7891. https://doi.org/10.3390/app13137891
Nunes, P., Santos, J., & Rocha, E. (2023). Challenges in Predictive Maintenance – A Review. CIRP Journal of Manufacturing Science and Technology, 40, 53–67. https://doi.org/10.1016/j.cirpj.2022.11.004
Padilla, R., Passos, W. L., Dias, T. L. B., Netto, S. L., & Da Silva, E. A. B. (2021). A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit. Electronics, 10(3), 279. https://doi.org/10.3390/electronics10030279
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., … Ziel, F. (2022). Forecasting: Theory and Practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001
Petter, S., DeLone, W., & McLean, E. (2008). Measuring Information Systems Success: Models, Dimensions, Measures, and Interrelationships. European Journal of Information Systems, 17(3), 236–263. https://doi.org/10.1057/ejis.2008.15
Romano, M., Contu, G., Mola, F., & Conversano, C. (2023). Threshold-Based Naïve Bayes Classifier. Advances in Data Analysis and Classification. https://doi.org/10.1007/s11634-023-00536-8
Salite, D., Kirshner, J., Cotton, M., Howe, L., Cuamba, B., Feijó, J., & Macome, A. Z. (2021). Electricity Access in Mozambique: A Critical Policy Analysis of Investment, Service Reliability and Social Sustainability. Energy Research & Social Science, 78, 102123. https://doi.org/10.1016/j.erss.2021.102123
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review. Cognitive Robotics, 3, 54–70. https://doi.org/10.1016/j.cogr.2023.04.001
Tian, Y., Zhang, Y., & Zhang, H. (2023). Recent Advances in Stochastic Gradient Descent in Deep Learning. Mathematics, 11(3), 682. https://doi.org/10.3390/math11030682
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing Different Supervised Machine Learning Algorithms for Disease Prediction. BMC Medical Informatics and Decision Making, 19(1), 281. https://doi.org/10.1186/s12911-019-1004-8
Van Dinter, R., Tekinerdogan, B., & Catal, C. (2022). Predictive Maintenance using Digital Twins: A Systematic Literature Review. Information and Software Technology, 151, 107008. https://doi.org/10.1016/j.infsof.2022.107008
Varshini, B., Yogesh, H., Pasha, S. D., Suhail, M., Madhumitha, V., & Sasi, A. (2021). IoT-Enabled Smart Doors for Monitoring Body Temperature and Face Mask Detection. Global Transitions Proceedings, 2(2), 246–254. https://doi.org/10.1016/j.gltp.2021.08.071
Vishwakarma, G., Sonpal, A., & Hachmann, J. (2021). Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry. Trends in Chemistry, 3(2), 146–156. https://doi.org/10.1016/j.trechm.2020.12.004
Author Biography
Alfian Akbar Gozali, Universitas Telkom
License
Copyright (c) 2023 Alfian Akbar Gozali
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
