Diagnostics and Prognostics of Boilers in Power Plant Based on Data-Driven and Machine Learning
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Published
Jan 31, 2025
Achmad Widodo
Toni Prahasto
Mochamad Soleh
Herry Nugraha
Abstract
This paper reports diagnostics and prognostics study of boiler in power plant using actual boiler operating data. This study aims to early detect anomalies that occur in the boiler and to predict the remaining useful life (RUL) after anomalies are detected. The proposed method utilizes machine learning techniques through support vector machine (SVM) and random forest algorithm (RFA) for anomaly detection and similarity-based method of dynamic time warping (DTW) for RUL prediction. The developed method is validated by testing the prediction models using real operating data acquired from three boilers in power plant. The results show that some anomalies are successfully detected by prediction model even though there are anomalies that give low accuracies in predictions. RUL prediction also provides fair results given the limitations of the real data used in building prediction models. Overall, the results of this study have potential to be applied in real system as an auxiliary tool in the boiler condition monitoring to support boiler maintenance programs.
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Keywords
power plant, anomaly detection, boiler, diagnostics, prognostics, machine learning
References
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Alegeh, N., Shagluf, A., Longstaff, A. P. & Fletcher, S. 2019. Accuracy In Detecting Failure In Ballscrew Assessment Towards Machine Tool Servitization. International Journal Of Mechanical Engineering And Robotics Research, 8, 667-673.
Babcock & Company, W. 1923. Steam: Its Generation And Use, Babcock & Wilcox Company.
Barr, A., Suard, F., Gérard, M. & Riu, D. A Real-Time Data-Driven Method For Battery Health Prognostics In Electric Vehicle Use. Phm Society European Conference, 2014.
Berahman, M., Safavi, A. & Shahrbabaki, M. R. Fault Detection In Kerman Combined Cycle Power Plant Boilers By Means Of Support Vector Machine Classifier Algorithms And Pca. The 3rd International Conference On Control, Instrumentation, And Automation, 2013. IEEE, 290-295.
Bertolini, M., Mezzogori, D., Neroni, M. & Zammori, F. 2021. Machine Learning For Industrial Applications: A Comprehensive Literature Review. Expert Systems With Applications, 175, 114820.
Breiman, L. 1996. Bagging Predictors. Machine Learning, 24, 123-140.
Breiman, L. 2001. Random Forests. Machine Learning, 45, 5-32.
Chen, K.-Y., Chen, L.-S., Chen, M.-C. & Lee, C.-L. 2011. Using Svm Based Method For Equipment Fault Detection In A Thermal Power Plant. Computers In Industry, 62, 42-50.
Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M. & Safaei, B. 2020. Machine Learning In Predictive Maintenance Towards Sustainable Smart Manufacturing In Industry 4.0. Sustainability, 12, 8211.
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Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J. & Barbosa, J. 2020. Machine Learning And Reasoning For Predictive Maintenance In Industry 4.0: Current Status And Challenges. Computers In Industry, 123, 103298.
Diez-Olivan, A., Del Ser, J., Galar, D. & Sierra, B. 2019. Data Fusion And Machine Learning For Industrial Prognosis: Trends And Perspectives Towards Industry 4.0. Information Fusion, 50, 92-111.
Do, P., Voisin, A., Levrat, E. & Iung, B. 2015. A Proactive Condition-Based Maintenance Strategy With Both Perfect And Imperfect Maintenance Actions. Reliability Engineering & System Safety, 133, 22-32.
Duong, B. P., Kim, J., Kim, C.-H. & Kim, J.-M. 2019. Deep Learning Object-Impulse Detection For Enhancing Leakage Detection Of A Boiler Tube Using Acoustic Emission Signal. Applied Sciences [Online], 9.
Dzikuć, M., Kuryło, P., Dudziak, R., Szufa, S., Dzikuć, M. & Godzisz, K. 2020. Selected Aspects Of Combustion Optimization Of Coal In Power Plants. Energies, 13, 2208.
Han, T., Jiang, D., Zhao, Q., Wang, L. & Yin, K. 2018. Comparison Of Random Forest, Artificial Neural Networks And Support Vector Machine For Intelligent Diagnosis Of Rotating Machinery. Transactions Of The Institute Of Measurement And Control, 40, 2681-2693.
Hong-Feng, W. 2012. Prognostics And Health Management For Complex System Based On Fusion Of Model-Based Approach And Data-Driven Approach. Physics Procedia, 24, 828-831.
Indrawan, N., Shadle, L. J., Breault, R. W., Panday, R. & Chitnis, U. K. 2021. Data Analytics For Leak Detection In A Subcritical Boiler. Energy, 220, 119667.
Khalid, S., Lim, W., Kim, H. S., Oh, Y. T., Youn, B. D., Kim, H.-S. & Bae, Y.-C. 2020. Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection Via Machine Learning-Based Optimal Sensor Selection. Sensors, 20, 6356.
Khan, P. W., Yeun, C. Y. & Byun, Y. C. 2023. Fault Detection Of Wind Turbines Using Scada Data And Genetic Algorithm-Based Ensemble Learning. Engineering Failure Analysis, 148, 107209.
Khan, T., Udpa, L. & Udpa, S. Particle Filter Based Prognosis Study For Predicting Remaining Useful Life Of Steam Generator Tubing. 2011 IEEE Conference On Prognostics And Health Management, 20-23 June 2011 2011. 1-6.
Lei, Y., Li, N., Guo, L., Li, N., Yan, T. & Lin, J. 2018. Machinery Health Prognostics: A Systematic Review From Data Acquisition To Rul Prediction. Mechanical Systems And Signal Processing, 104, 799-834.
Li, Z., Liu, R. & Wu, D. 2019. Data-Driven Smart Manufacturing: Tool Wear Monitoring With Audio Signals And Machine Learning. Journal Of Manufacturing Processes, 48, 66-76.
Liao, L. & Köttig, F. 2014. Review Of Hybrid Prognostics Approaches For Remaining Useful Life Prediction Of Engineered Systems, And An Application To Battery Life Prediction. IEEE Transactions On Reliability, 63, 191-207.
Lin, W. C. & Ghoneim, Y. A. Model-Based Fault Diagnosis And Prognosis For Electric Power Steering Systems. 2016 IEEE International Conference On Prognostics And Health Management (Icphm), 20-22 June 2016 2016. 1-8.
Madrigal-Espinosa, G., Osorio-Gordillo, G. L., Astorga-Zaragoza, C. M., Vázquez-Román, M. & Adam-Medina, M. 2017. Fault Detection And Isolation System For Boiler-Turbine Unit Of A Thermal Power Plant. Electric Power Systems Research, 148, 237-244.
Martinez, A. M. & Kak, A. C. 2001. Pca Versus Lda. IEEE Transactions On Pattern Analysis And Machine Intelligence, 23, 228-233.
Montero Jimenez, J. J., Schwartz, S., Vingerhoeds, R., Grabot, B. & Salaün, M. 2020. Towards Multi-Model Approaches To Predictive Maintenance: A Systematic Literature Survey On Diagnostics And Prognostics. Journal Of Manufacturing Systems, 56, 539-557.
Mushiri, T., Mhazo, T. K. & Mbohwa, C. 2018. Condition Based Monitoring Of Boiler Parameters In A Thermal Power Station (Case Of Anonymous Company). Procedia Manufacturing, 21, 369-375.
Nguyen, H.-P., Fauriat, W., Zio, E. & Liu, J. A Data-Driven Approach For Predicting The Remaining Useful Life Of Steam Generators. 2018 3rd International Conference On System Reliability And Safety (Icsrs), 2018. IEEE, 255-260.
Orrù, P. F., Zoccheddu, A., Sassu, L., Mattia, C., Cozza, R. & Arena, S. 2020. Machine Learning Approach Using Mlp And Svm Algorithms For The Fault Prediction Of A Centrifugal Pump In The Oil And Gas Industry. Sustainability, 12, 4776.
Panday, R., Indrawan, N., Shadle, L. J. & Vesel, R. W. 2021. Leak Detection In A Subcritical Boiler. Applied Thermal Engineering, 185, 116371.
Que, Z. & Xu, Z. 2019. A Data-Driven Health Prognostics Approach For Steam Turbines Based On Xgboost And Dtw. IEEE Access, 7, 93131-93138.
Sarkar, D. 2015. Thermal Power Plant: Design And Operation, Elsevier.
Sbarufatti, C., Corbetta, M., Manes, A. & Giglio, M. 2016. Sequential Monte-Carlo Sampling Based On A Committee Of Artificial Neural Networks For Posterior State Estimation And Residual Lifetime Prediction. International Journal Of Fatigue, 83, 10-23.
Shohet, R., Kandil, M. S. & Mcarthur, J. Machine Learning Algorithms For Classification Of Boiler Faults Using A Simulated Dataset. Iop Conference Series: Materials Science And Engineering, 2019. Iop Publishing, 062007.
Shohet, R., Kandil, M. S., Wang, Y. & Mcarthur, J. J. 2020. Fault Detection For Non-Condensing Boilers Using Simulated Building Automation System Sensor Data. Advanced Engineering Informatics, 46, 101176.
Sohaib, M. & Kim, J.-M. 2019. Data Driven Leakage Detection And Classification Of A Boiler Tube. Applied Sciences [Online], 9.
Sola, J. & Sevilla, J. 1997. Importance Of Input Data Normalization For The Application Of Neural Networks To Complex Industrial Problems. IEEE Transactions On Nuclear Science, 44, 1464-1468.
Świercz, M. & Mroczkowska, H. 2019. Application Of Pca For Early Leak Detection In A Pipeline System Of A Steam Boiler. Przegląd Elektrotechniczny.
Tao, L., Lu, C. & Noktehdan, A. 2015. Similarity Recognition Of Online Data Curves Based On Dynamic Spatial Time Warping For The Estimation Of Lithium-Ion Battery Capacity. Journal Of Power Sources, 293, 751-759.
Tin Kam, H. Random Decision Forests. Proceedings Of 3rd International Conference On Document Analysis And Recognition, 14-16 Aug. 1995 1995. 278-282 Vol.1.
Vapnik, V. 2013. The Nature Of Statistical Learning Theory, Springer Science & Business Media.
Wang, Y., Yin, X. & Wang, B. A Method Of Diagnosing Leakage Of Boiler Steam And Water Pipes Based On Genetic Neural Network. 2016 12th World Congress On Intelligent Control And Automation (Wcica), 2016. IEEE, 2829-2833.
Alegeh, N., Shagluf, A., Longstaff, A. P. & Fletcher, S. 2019. Accuracy In Detecting Failure In Ballscrew Assessment Towards Machine Tool Servitization. International Journal Of Mechanical Engineering And Robotics Research, 8, 667-673.
Babcock & Company, W. 1923. Steam: Its Generation And Use, Babcock & Wilcox Company.
Barr, A., Suard, F., Gérard, M. & Riu, D. A Real-Time Data-Driven Method For Battery Health Prognostics In Electric Vehicle Use. Phm Society European Conference, 2014.
Berahman, M., Safavi, A. & Shahrbabaki, M. R. Fault Detection In Kerman Combined Cycle Power Plant Boilers By Means Of Support Vector Machine Classifier Algorithms And Pca. The 3rd International Conference On Control, Instrumentation, And Automation, 2013. IEEE, 290-295.
Bertolini, M., Mezzogori, D., Neroni, M. & Zammori, F. 2021. Machine Learning For Industrial Applications: A Comprehensive Literature Review. Expert Systems With Applications, 175, 114820.
Breiman, L. 1996. Bagging Predictors. Machine Learning, 24, 123-140.
Breiman, L. 2001. Random Forests. Machine Learning, 45, 5-32.
Chen, K.-Y., Chen, L.-S., Chen, M.-C. & Lee, C.-L. 2011. Using Svm Based Method For Equipment Fault Detection In A Thermal Power Plant. Computers In Industry, 62, 42-50.
Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M. & Safaei, B. 2020. Machine Learning In Predictive Maintenance Towards Sustainable Smart Manufacturing In Industry 4.0. Sustainability, 12, 8211.
Cui, Y. J., Xia, L. W., Huang, Y. & Ma, X. C. Research On Fault Diagnosis And Early Warning Of Power Plant Boiler Reheater Temperature Deviation Based On Machine Learning Algorithm. 2020 IEEE 6th International Conference On Control Science And Systems Engineering (Iccsse), 17-19 July 2020 2020. 212-216.
Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J. & Barbosa, J. 2020. Machine Learning And Reasoning For Predictive Maintenance In Industry 4.0: Current Status And Challenges. Computers In Industry, 123, 103298.
Diez-Olivan, A., Del Ser, J., Galar, D. & Sierra, B. 2019. Data Fusion And Machine Learning For Industrial Prognosis: Trends And Perspectives Towards Industry 4.0. Information Fusion, 50, 92-111.
Do, P., Voisin, A., Levrat, E. & Iung, B. 2015. A Proactive Condition-Based Maintenance Strategy With Both Perfect And Imperfect Maintenance Actions. Reliability Engineering & System Safety, 133, 22-32.
Duong, B. P., Kim, J., Kim, C.-H. & Kim, J.-M. 2019. Deep Learning Object-Impulse Detection For Enhancing Leakage Detection Of A Boiler Tube Using Acoustic Emission Signal. Applied Sciences [Online], 9.
Dzikuć, M., Kuryło, P., Dudziak, R., Szufa, S., Dzikuć, M. & Godzisz, K. 2020. Selected Aspects Of Combustion Optimization Of Coal In Power Plants. Energies, 13, 2208.
Han, T., Jiang, D., Zhao, Q., Wang, L. & Yin, K. 2018. Comparison Of Random Forest, Artificial Neural Networks And Support Vector Machine For Intelligent Diagnosis Of Rotating Machinery. Transactions Of The Institute Of Measurement And Control, 40, 2681-2693.
Hong-Feng, W. 2012. Prognostics And Health Management For Complex System Based On Fusion Of Model-Based Approach And Data-Driven Approach. Physics Procedia, 24, 828-831.
Indrawan, N., Shadle, L. J., Breault, R. W., Panday, R. & Chitnis, U. K. 2021. Data Analytics For Leak Detection In A Subcritical Boiler. Energy, 220, 119667.
Khalid, S., Lim, W., Kim, H. S., Oh, Y. T., Youn, B. D., Kim, H.-S. & Bae, Y.-C. 2020. Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection Via Machine Learning-Based Optimal Sensor Selection. Sensors, 20, 6356.
Khan, P. W., Yeun, C. Y. & Byun, Y. C. 2023. Fault Detection Of Wind Turbines Using Scada Data And Genetic Algorithm-Based Ensemble Learning. Engineering Failure Analysis, 148, 107209.
Khan, T., Udpa, L. & Udpa, S. Particle Filter Based Prognosis Study For Predicting Remaining Useful Life Of Steam Generator Tubing. 2011 IEEE Conference On Prognostics And Health Management, 20-23 June 2011 2011. 1-6.
Lei, Y., Li, N., Guo, L., Li, N., Yan, T. & Lin, J. 2018. Machinery Health Prognostics: A Systematic Review From Data Acquisition To Rul Prediction. Mechanical Systems And Signal Processing, 104, 799-834.
Li, Z., Liu, R. & Wu, D. 2019. Data-Driven Smart Manufacturing: Tool Wear Monitoring With Audio Signals And Machine Learning. Journal Of Manufacturing Processes, 48, 66-76.
Liao, L. & Köttig, F. 2014. Review Of Hybrid Prognostics Approaches For Remaining Useful Life Prediction Of Engineered Systems, And An Application To Battery Life Prediction. IEEE Transactions On Reliability, 63, 191-207.
Lin, W. C. & Ghoneim, Y. A. Model-Based Fault Diagnosis And Prognosis For Electric Power Steering Systems. 2016 IEEE International Conference On Prognostics And Health Management (Icphm), 20-22 June 2016 2016. 1-8.
Madrigal-Espinosa, G., Osorio-Gordillo, G. L., Astorga-Zaragoza, C. M., Vázquez-Román, M. & Adam-Medina, M. 2017. Fault Detection And Isolation System For Boiler-Turbine Unit Of A Thermal Power Plant. Electric Power Systems Research, 148, 237-244.
Martinez, A. M. & Kak, A. C. 2001. Pca Versus Lda. IEEE Transactions On Pattern Analysis And Machine Intelligence, 23, 228-233.
Montero Jimenez, J. J., Schwartz, S., Vingerhoeds, R., Grabot, B. & Salaün, M. 2020. Towards Multi-Model Approaches To Predictive Maintenance: A Systematic Literature Survey On Diagnostics And Prognostics. Journal Of Manufacturing Systems, 56, 539-557.
Mushiri, T., Mhazo, T. K. & Mbohwa, C. 2018. Condition Based Monitoring Of Boiler Parameters In A Thermal Power Station (Case Of Anonymous Company). Procedia Manufacturing, 21, 369-375.
Nguyen, H.-P., Fauriat, W., Zio, E. & Liu, J. A Data-Driven Approach For Predicting The Remaining Useful Life Of Steam Generators. 2018 3rd International Conference On System Reliability And Safety (Icsrs), 2018. IEEE, 255-260.
Orrù, P. F., Zoccheddu, A., Sassu, L., Mattia, C., Cozza, R. & Arena, S. 2020. Machine Learning Approach Using Mlp And Svm Algorithms For The Fault Prediction Of A Centrifugal Pump In The Oil And Gas Industry. Sustainability, 12, 4776.
Panday, R., Indrawan, N., Shadle, L. J. & Vesel, R. W. 2021. Leak Detection In A Subcritical Boiler. Applied Thermal Engineering, 185, 116371.
Que, Z. & Xu, Z. 2019. A Data-Driven Health Prognostics Approach For Steam Turbines Based On Xgboost And Dtw. IEEE Access, 7, 93131-93138.
Sarkar, D. 2015. Thermal Power Plant: Design And Operation, Elsevier.
Sbarufatti, C., Corbetta, M., Manes, A. & Giglio, M. 2016. Sequential Monte-Carlo Sampling Based On A Committee Of Artificial Neural Networks For Posterior State Estimation And Residual Lifetime Prediction. International Journal Of Fatigue, 83, 10-23.
Shohet, R., Kandil, M. S. & Mcarthur, J. Machine Learning Algorithms For Classification Of Boiler Faults Using A Simulated Dataset. Iop Conference Series: Materials Science And Engineering, 2019. Iop Publishing, 062007.
Shohet, R., Kandil, M. S., Wang, Y. & Mcarthur, J. J. 2020. Fault Detection For Non-Condensing Boilers Using Simulated Building Automation System Sensor Data. Advanced Engineering Informatics, 46, 101176.
Sohaib, M. & Kim, J.-M. 2019. Data Driven Leakage Detection And Classification Of A Boiler Tube. Applied Sciences [Online], 9.
Sola, J. & Sevilla, J. 1997. Importance Of Input Data Normalization For The Application Of Neural Networks To Complex Industrial Problems. IEEE Transactions On Nuclear Science, 44, 1464-1468.
Świercz, M. & Mroczkowska, H. 2019. Application Of Pca For Early Leak Detection In A Pipeline System Of A Steam Boiler. Przegląd Elektrotechniczny.
Tao, L., Lu, C. & Noktehdan, A. 2015. Similarity Recognition Of Online Data Curves Based On Dynamic Spatial Time Warping For The Estimation Of Lithium-Ion Battery Capacity. Journal Of Power Sources, 293, 751-759.
Tin Kam, H. Random Decision Forests. Proceedings Of 3rd International Conference On Document Analysis And Recognition, 14-16 Aug. 1995 1995. 278-282 Vol.1.
Vapnik, V. 2013. The Nature Of Statistical Learning Theory, Springer Science & Business Media.
Wang, Y., Yin, X. & Wang, B. A Method Of Diagnosing Leakage Of Boiler Steam And Water Pipes Based On Genetic Neural Network. 2016 12th World Congress On Intelligent Control And Automation (Wcica), 2016. IEEE, 2829-2833.
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Technical Papers