Detection and Diagnostic with Random Forest Classifier (RFC) to Improve the Maintenance Management System in Steam Boiler of Power Plant



Published Sep 4, 2023
Ghiffari Awliya Muhammad Ashfania Tarwaji Warsokusumo Toni Prahasto Achmad Widodo


Industrial internet of things (IIoT), digital twin, and connected devices can continue to use smart equipment and improve access to data. While the data collected by sensors has been an invaluable asset to companies, the ability to understand and use this data to drive new insights. The development of Condition Monitoring (CM) technology and Computerized Maintenance Management System (CMMS) in power generation systems provides a validated set of operation and maintenance data with abundant event data. Maintenance decision-making is primarily based on equipment reliability and performance-based features for diagnosing equipment failure. The most critical asset and often reduces the reliability and availability of a Coal Fired Steam Power Plant (CFSPP) with the most frequency of disturbances is the steam boiler. As a departure from the idea of creating a digital twin, this article will focus on analyzing equipment health conditions and finding causes of failure of the tools, utilizing data for diagnostic purposes. Real-case used in this research are steam boilers, which are important assets in power plant generation. The online and Failure Mode and Effect Analysis (FMEA) module data will be combined to realize the concept of anomaly diagnosis which is driven by hybrid data. Hoping that accurate diagnosis result with the Random Forest Classifier (RFC) Algorithm can be obtained and be used to analyze the causes of failure and decrease in equipment performance resulting by a decrease of energy efficiency performance. The analytical approaches are carried out to have the goal of generating detection models and diagnostic insights of event data based on operational data and FMEA.

Abstract 159 | PDF Downloads 144



Failure Detection, Anomaly Diagnostic, Random Forest Classifier, Maintenance Management System

Ansari, F., Glawar, R., & Nemeth, T. (2019). PriMa: a prescriptive maintenance model for cyber-physical production systems. International Journal of Computer Integrated Manufacturing, 32(4–5), 482–503.

Bhattacharjee, P., Dey, V., & Mandal, U. K. (2020). Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model. Safety Science, 132.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Breiman, L., Friedman, J., Olshen, R., & Stone, C. (2017). Classification And Regression Trees. In Classification and Regression Trees.

Dhini, A., Kusumoputro, B., & Surjandari, I. (2017). Neural network based system for detecting and diagnosing faults in steam turbine of thermal power plant. Proceedings 2017 IEEE 8th International Conference on Awareness Science and Technology, ICAST 2017, 2018-Janua, 149–154.

Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020). Digital Twin for maintenance: A literature review. Computers in Industry, 123.

Hlady, J., Glanzer, M., & Fugate, L. (2018). Automated creation of the pipeline digital twin during construction Improvement to construction quality and pipeline integrity. Proceedings of the Biennial International Pipeline Conference, IPC, 2.

Jones, M. D., Hutcheson, S., & Camba, J. D. (2021). Past, present, and future barriers to digital transformation in manufacturing: A review. Journal of Manufacturing Systems, 60, 936–948.

Kent, R. (2016). Design quality management. In Quality Management in Plastics Processing (pp. 227–262) Elsevier. 1.50008-3

Li, M., Deng, W., Xiahou, K., Ji, T., & Wu, Q. (2020). A data-driven method for fault detection and isolation of the integrated energy-based district heating system. IEEE Access, 8, 23787–23801.

Mohanty, J. K., Dash, P. R., & Pradhan, P. K. (2020). FMECA analysis and condition monitoring of critical equipments in super thermal power plant. International Journal of System Assurance Engineering and Management, 11(3), 583–599.

Musolf, A. M., Holzinger, E. R., Malley, J. D., & BaileyWilson, J. E. (2022). What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics. Human Genetics, 141(9), 1515–1528. 021-02402-z

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.

Onu, P., & Mbohwa, C. (2021). Reimagining the future: Techno innovation advancement in manufacturing. Materials Today: Proceedings, 44, 1953–1959.

Shubenkova, K., Valiev, A., Mukhametdinov, E., Shepelev, V., Tsiulin, S., & Reinau, K. H. (2018). Possibility of Digital Twins Technology for Improving Efficiency of the Branded Service System. Proceedings 2018 Global Smart Industry Conference, GloSIC 2018.

Wang, H., Peng, M.-J., Wesley Hines, J., Zheng, G.-Y., Liu, Y.-K., & Upadhyaya, B. R. (2019). A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants. ISA Transactions, 95, 358–371.

Wang, Y., & Yin, X.-L. (2017). A Method of Diagnosing Boiler Four-Tube Leakage Rate: Proceedings of the International Conference on Environmental Science and Sustainable ZhaoYang Dong. In ESSE 2017. 053

Yin, X., & Wang, Y. (2015). Research on Fuzzy Diagnosis Method of Boiler Steam and Water Pipe Leakage. In Mechanical Engineering and Control Systems (pp. 75– 78). WORLD SCIENTIFIC.

Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers and Industrial Engineering, 150.
Regular Session Papers