Helmi Qosim
ABSTRACT
Synthesis loop is one of the critical systems in ammonia plant. Therefore, there is urgency for maintaining the reliability and availability of this system. Most of the shutdown events occur suddenly after the alarm is reached. So, there needs to be an early detection system to ensure anomaly problem captured by the operator before touching the alarm settings. The implementation of machine learning algorithms in making fault detection models has been used in various industries and objects. The algorithm used is the basic and ensemble classifier to compare which algorithms generate the best classification results. This research can provide a new idea and perspective into ammonia plant industry to prevent unscheduled shutdown by utilizing data using machine learning algorithm.
- Evans, P.C. & Annunziata, M. "Industrial internet: pushing the boundaries of minds and machines". GE Energy. 2012.Google Scholar
- Boyes, H., Hallaq, B., Cunningham, J., Watson, T. (2018). The industrial internet of things (IIoT): an analysis framework. Comput. Ind. 101, 1--12. doi:10.1016/j.compind.2018.04.015Google ScholarCross Ref
- Hagner, Thomas. (2009). The Alchemy of Air. New York: Broadway Books.Google Scholar
- Williamson, J. "Unplanned downtime affecting 82% of businesses. Accessed 25 Jan 2019Google Scholar
- Performance Report year period 2017-2019. Jakarta: PT Pupuk Indonesia (Persero).Google Scholar
- Amoee, G., M. Behrouz, B. Malihe. (2011). A comparison between data mining prediction algorithms for fault detection (Case study: Ahanpishegan co.). IJCSI International Journal of Computer Sciences Issues. 8, (3): 425--431.Google Scholar
- Bruton, K., Coakley, D., Raftery, P., Cusack, D. O., Keane, M. M., & O'Sullivan, D. T. J. (2015). Comparative analysis of the ahu info fault detection and diagnostic expert tool for ahus with apar. Energy Efficiency, 8(2), 299--322. doi:10.1007/s12053-014-9289-zGoogle ScholarCross Ref
- Purarjomandlangrudi, A., Ghapanchi, A. H., & Esmalifalak, M. (2014). A data mining approach for fault diagnosis: An application of anomaly detection algorithm. Measurement 55, 343--352. doi: 10.1016/j.measurement.2014.05.029Google ScholarCross Ref
- Saravanan, N., Siddabattuni, V. N. S. K., & Ramachandran, K. I. (2010). Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM). Applied Soft Computing. 10 (1): 344--360. doi:10.1016/j.asoc.2009.08.006Google ScholarDigital Library
- Yang, C., Liu, J., Zeng, Y., & Xie, G. (2019). Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model. Renewable Energy 133, 433--441. doi:10.1016/j.renene.2018.10.062Google ScholarCross Ref
- Qi, G., Zhu, Z., Erqinhu, K., Chen, Y., Chai, Y., & Sun, J. (2018). Fault-diagnosis for reciprocating compressors using big data and machine learning. Simulation Modelling Practice and Theory. 80: 104--127. doi:10.1016/j.simpat.2017.10.005Google ScholarCross Ref
- Santos, P., Villa, L. F., Reñones, A., Bustillo, A., & Maudes, J. (2015). An SVM-Based Solution for Fault Detection in Wind Turbines. Sensors. 15 (3): 5627--5648. doi:10.3390/s150305627Google ScholarCross Ref
- Wise, B. M., Gallagher, N. B., Butler, S. W., White Jr., D. D., & Barna, G. G. (1999). A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics, 13(3-4), 379--396. doi:10.1002/(SICI)1099-128X(199905/08)13:3/4<379::AID-CEM556>3.0.CO;2-NGoogle ScholarCross Ref
- Kresta, J. V., Macgregor, J. F., & Marlin, T. E. (1991). Multivariate statistical monitoring of process operating performance. Canadian Journal of Chemical Engineering, 69(1), 35--47. doi:10.1002/cjce.5450690105Google ScholarCross Ref
- Wong, P. K., Yang, Z., Vong, C. M., & Zhong, J. (2014). Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing. 128: 249--257. doi:10.1016/j.neucom.2013.03.059Google ScholarDigital Library
- Henmi, T., Inoue, A., Deng, M., & Yoshinaga, S. -. (2016). Early detection of plant faults by using machine learning. InAdvanced Mechatronic Systems (ICAMechS)," 2016, International Conference on 2016, Nov 30 (pp. 201--205). doi:10.1109/ICAMechS.2016.7813447Google Scholar
- Katipamula, S., & Brambley, M. R. (2005). Methods for Fault Detection, Diagnostics and Prognostics for Building Systems - A Review Part I. HVAC and R Research. 11: 34--48. doi:10.1080/10789669.2005.10391123Google Scholar
- Chen, Xiangrui. (2018). Anomaly detection of petrochemical process engineering based on neural network. Chemical Engineering Transactions Vol 71, 2018. doi:10.3303/CET1871044Google Scholar
- Wu, H & Zhao, J. (2018). Deep convolutional neural network model based chemical process fault diagnosis. Computers and Chemical Engineering. doi: 10.1016./j.compchemeng.2018.04.009.Google Scholar
- Kini, K. R., & Madakyaru, M. (2019). Anomaly detection using multi-scale dynamic principal component analysis for tenneesse eastman process. Indian Control Conference (ICC) 2019. doi:10.1109/INDIANCC.2019.8715552Google ScholarCross Ref
- Zulkarnain, Surjandari, I., Bramasta, R. R., & Laoh, E. (2019). Fault detection using machine learning on geothermal power plant. 16th International Conference on Service Systems and Service Management, ICSSSM 2019, doi:10.1109/ICSSSM.2019.8887710.Google ScholarCross Ref
- Kim, K. H.; Seok Lee, H., Kim, J. H.; & Ho Park, J. (2019). Detection of boiler tube leakage fault in a thermal power plant using machine learning based data mining technique. International Conference on Industrial Technology (ICIT) 2019. doi:10.1109/ICIT.2019.8755058Google ScholarCross Ref
- Lejla Banjanovic-Mehmedovic et al. (2017). Nerual network-based data-driven modelling of anomaly detection in thermal power plant. Automatika. 58: 1, 69--79.Google ScholarCross Ref
- Dhini, A., Kusumoputro, B., & Surjandari, I. (2017). Neural network based system for detecting and diagnosing faults in steam turbine of thermal power plant. International Conference on Awareness Science and Technology (iCAST) 2017. doi:10.1109/ICAwST.2017.8256435Google ScholarCross Ref
- Yin, S., Zhu, X., & Jing, C. (2014). Fault detection based on a robust one class support vector machine. Neurocomputing. 124: 263--268. doi:10.1016/j.neucom.2014.05.035Google ScholarCross Ref
- Accorsi, R., Manzini, R., Pascarella, P., Patella, M., & Sassi, S. (2017). Data mining and machine learning for condition-based maintenance. Procedia Manufacturing 11, 11531161. doi:10.1016/j.promfg.2017.07.239Google ScholarCross Ref
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning. 20 (3): 273--297. doi:10.1023/A:1022627411411Google ScholarCross Ref
- Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006). Machine learning: a review of classification and combining techniques. Artificial Intelligence Review. 26 (3): 159--190. doi:10.1007/s10462-007-9052-3Google ScholarDigital Library
- Guo, J., Wang, X., & Li, Y. (2018). kNN based on probability density for fault detection in multimodal process. Journal of Wiley Chemometrics, DOI: 10.1002/cem.3021Google Scholar
- He, S., Wang, G. A., Zhang, M., & Cook, D. F. (2013). Multivariate process monitoring and fault identification using multiple decision tree classifiers. International Journal of Production Research, DOI: 10.1080/00207543.2013.774474Google ScholarCross Ref
- Hu, Q., Si, X. -., Zhang, Q. -., & Qin, A. -. (2020). A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forest. Mechanical System and Signal Processing 139 (2020) 106609. doi:10.1016/j.ymssp.2019.106609Google ScholarCross Ref
- Shrivastava, R., Mahalingam, H., & Dutta, N. N. (2017). Application and evaluation of random forest classifier technique for fault detection in bioreactor operation. Chemical Engineering Communications, doi: 10.1080/00986445.2017.1292259Google Scholar
- Larose, Daniel T. & Larose, Chantal D. (2015). Data Mining and Predictive Analysis. John Willey & Sons, IncGoogle Scholar
- Zhang, D., Qian, L., Mao, B., Huang, C., Huang, B., & Si, Y. (2018). A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE Access, 6, 21020--21031. doi:10.1109/ACCESS.2018.2818678Google ScholarCross Ref
- Tsaganos, G., Nikitakos, N., Dalaklis, D., Ölcer, A. I., & Papachristos, D. (2020). Machine learning algorithms in shipping: Improving engine fault detection and diagnosis via ensemble methods. WMU Journal of Maritime Affairs, doi:10.1007/s13437-019-00192-wGoogle Scholar
Index Terms
- Fault Detection System Using Machine Learning on Synthesis Loop Ammonia Plant
Recommendations
Anomaly detection for fault detection in wireless community networks using machine learning
AbstractMachine learning has received increasing attention in computer science in recent years and many types of methods have been proposed. In computer networks, little attention has been paid to the use of ML for fault detection, the main reason being ...
Intrusion Detection System Using Bagging Ensemble Method of Machine Learning
ICCUBEA '15: Proceedings of the 2015 International Conference on Computing Communication Control and AutomationIntrusion detection system is widely used to protect and reduce damage to information system. It protects virtual and physical computer networks against threats and vulnerabilities. Presently, machine learning techniques are widely extended to implement ...
Detection of false sharing using machine learning
SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and AnalysisFalse sharing is a major class of performance bugs in parallel applications. Detecting false sharing is difficult as it does not change the program semantics. We introduce an efficient and effective approach for detecting false sharing based on machine ...
Comments