Predictive Maintenance in SCADA-Based Industries: A literature review
DOI:
https://doi.org/10.24002/ijieem.v2i1.4368Keywords:
predictive maintenance, Supervisory Control and Data Acquisition (SCADA), Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML)Abstract
The purpose of this paper is to mapping and review what has been done on the topic of research on predictive maintenance in SCADA (Supervisory Control and Data Acquisition) based industries. In the research area of predictive maintenance, various methods for predicting damage or time to failure of a machine have been proposed and applied in various industries. This paper systematically categorizes predictive maintenance in SCADA-based industries research based on industry classifications according to ISIC (International Standard Industrial Classification of All Economic Activities). Furthermore, the research scope is explored its connection to the topics of Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Supervisory Control and Data Acquisition (SCADA). It is found that 81.5% of the research was conducted on the electricity, gas, steam, and air conditioning supply industries, 11.1% of research was conducted on the mining and quarrying industry, and 7.4% of the research conducted in the manufacturing industry. It is also found that 85.2% of studies used AI and ML, 18.5% of the studies used IoT, and 18.5% of research used AI/ML and IoT technology together.
References
Ajami, H. & Gosine, A. (2015). Migrating to digital bus technology. Water/Wastewater and Automatic Controls Symposium 2015 (WWAC 2015), 1, 21-24.
Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., Tran, B., Xue, B., Zhang, M. (2019). A Survey on Evolutionary Machine Learning. Journal of The Royal Society of New Zealand, 49(2), 205-228.
Al-Turjman, F. (2017). Cognitive Sensors and IoT - Architecture, Deployment, and Data Delivery. CRC Press, Inc: New York.
Bangalore, P. & Tjernberg, L. (2013). An approach for self evolving neural network based algorithm for fault prognosis in wind turbine. 2013 IEEE Grenoble Conference PowerTech (POWERTECH 2013), art. no. 6652218.
Bangalore, P. & Tjernberg, L. (2015). An artificial neural network approach for early fault detection of gearbox bearings. IEEE Transactions on Smart Grid, 6(2), 980-987.
Berardis, S. & Donisi, D. (2018). Real-time structural health monitoring system of osmangazi bridge (Turkey). IABSE Conference, Copenhagen 2018: Engineering the Past, to Meet the Needs of the Future – Report, 423-430.
Besterfield, D. H., 2013. Quality Improvement. 9th ed. Pearson Education Inc.
Boyer, S. A., 2004. Supervisory Control and Data Acquisition. The Instrumentation, System, and Automation Society.
Carter, C. (2006). Record collection. Engineer, 293(7704), 41-42.
Carter, C. (2008). Save and prosper. Engineer, 293(7756), 62-66.
Colone, L., Dimitrov, N. & Straub, D. (2019) Predictive repair scheduling of wind turbine drive-train components based on machine learning. Wind Energy, 22(9), 1230-1242.
De La Hermosa González-Carrato, R., Márquez, F. & Papaelias, M. (2013). Use of novel algorithms for Predictive Maintenance in wind turbines. 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2013 and MFPT 2013), 310-322.
De La Hermosa Gonzalez-Carrato, R.R., Márquez, F.P.G., Pérez, J.M.P., Papaelias, M., Entezami, M. (2012). A novel predictive maintenance method for wind turbines based on wavelets transforms. 9th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM and MFPT 2012), 15-27.
Gigoni, L., Betti, A., Tucci, M. & Crisostomi, E., (2019). A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA. IEEE Power and Energy Society General Meeting, 8973898.
Godwin, J. & Matthews, P. (2013). Classification and detection of wind turbine pitch faults through SCADA data analysis. International Journal of Prognostics and Health Management, 4(S2). art. no. 16
Godwin, J., Matthews, P. & Watson, C. (2013). Classification and detection of electrical control system faults through SCADA data analysis. Chemical Engineering Transactions, 33, 985-990.
Gonzalez, D., Zatica, U., Gallastegi, A. & Cormenzana, M. (2014). Diagnosis of capacitive voltage transformers in service New method to determine the accuracy of CVTs while in service, without physical references and with a light portable unit. CIGRE Session 45 - 45th International Conference on Large High Voltage Electric Systems.
Grosch, R. & Bilot, P. (2002). A simple strategy for integrated plant management and control. ISA Instrumentation, Systems, and Automation Conference Proceedings, 13-23.
Hagner, S. & Bujak, J. (2016). Optimizing the network and the asset lifecycle and reducing operational and capital costs through predictive analytics and asset health management. CIGRE Session 46.
Hwang, K. & Chen, M. (2017). Big-Data Analytics for Cloud, IoT and Cognitive Computing. John Wiley & Sons. The UK.
Ignat, S., Stancel, E. & Stoian, I. (2012). Support for condition based maintenance: Operating equipment performances monitoring. IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2012 – Proceedings, 234-239.
Khatib, A.-R., Dong, X., Qiu, B. & Liu, Y. (2000). Thoughts on future internet based power system information network architecture. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 155-160.
Kiangala, K. & Wang, Z. (2018). Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts. International Journal of Advanced Manufacturing Technology, 97(9-12), 3251-3271.
Leahy, K., Gallagher, C., O'Donovan, P., Bruton, K., O'Sullivan, D.T.J. (2018). A robust prescriptive framework and performance metric for diagnosing and predicting wind turbine faults based on SCADA and alarms data with case study. Energies, 11(7), 1738.
Lebranchu, A., Charbonnier, S., Bérenguer, C. & Prevost, F. (2015). Review and analysis of SCADA data-based methods for health monitoring of wind turbines. Europe, Safety and Reliability of Complex Engineered Systems - Proceedings of the 25th European Safety and Reliability Conference, ESREL 2015, 2413-2421.
Lebranchu, A., Charbonnier, S., Berenguer, C. & Prevost, F. (2016). Using SCADA data for fault detection in wind turbines: Local internal model versus distance to a wind farm reference. Applied Condition Monitoring, 4, 225-236.
Liker, J. K. & Meier, D. (2006). The Toyota Way Fieldbook. McGraw-Hill. USA
Mazur, D., Kay, J. & Mazur, K. (2014). Advancements in vibration monitoring for the mining industry. IEEE Industry Application Society Annual Meeting, IAS, 6978459.
Mobley, R. K. (2002). An Introduction to Predictive Maintenance. Butterworth-Heinemann, USA.
Moleda, M., Momot, A. & Mrozek, D. (2020). Predictive Maintenance of Boiler Feed Water Pumps. Sensors, 20(2), 571.
Montague, J. (2015). Joining forces. Control, 28(9), 32-39.
NASA (2000). Reliability Centered Maintenance Guide for Facilities and Collateral Equipment. National Aeronautics and Space Administration, Washington DC.
Nisi, M., Renga, D., Apiletti, D., Giordano, D., Huang, T., Zhang, Y., Mellia, M., Baralis, E. (2019). Transparently mining data from a medium-voltage distribution network: A prognostic-diagnostic analysis. Lisbon, Portugal, CEUR Workshop Proceedings, 2322.
Nuñez, J., Pina, I.F.B., Martínez, A.R., Pérez, S.D., Oliveira, D.L. (2019). Tools for the Implementation of a SCADA System in a Desalination Process. IEEE Latin America Transactions, 17(11), 1858-1864.
Olivencia Polo, F., Alonso Del Rosario, J. & Cerruela García, G. (2010). Supervisory control and automatic failure detection in grid-connected photovoltaic systems. Lecture Notes in Computer Science, 6096, 458-467.
Perez, E. (2010). Fatigue counters for predictive maintenance. European Wind Energy Conference and Exhibition, 5, 3999-4009.
Renga, D., Apiletti, D., Giordano, D., Nisi, M., Huang, T., Zhang, Y., Mellia, M., Baralis, E. (2020). Data-driven exploratory models of an electric distribution. Computing. 102(5), 1199-1211.
Rensburg, N. (2019a). Usage of artificial intelligence to reduce operational disruptions of ESPs by implementing predictive maintenance. Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference.
Rensburg, N. (2019b). Autonomous well surveillance for ESP pumps using artificial intelligence. Society of Petroleum Engineers - SPE Oil and Gas India Conference and Exhibition.
Richard, E. (2015). Getting the most from your SCADA data. Water/Wastewater and Automatic Controls Symposium, 185-193.
Sánchez, L. & Couso, I. (2012). Singular spectral analysis of ill-known signals and its application to predictive maintenance of windmills with SCADA records. Soft Computing, 16(5), 755-768.
Schafer, K. (2000). Preventive maintenance plan optimized through automation. Part 2. Data acquisition, trend analysis systems should be integrated with program via computer. Pipe Line and Gas Industry, 83(2), 53-56.
Shalev-Shwartz, S. & Ben-David, S. (2014). Understanding Machine Learning From Theory to Algorithms. Cambridge University, USA.
Singh, A., Bapat, J. & Das, D. (2013). Distributed health monitoring system for control in Smart Grid network. IEEE Innovative Smart Grid Technologies - Asia, ISGT Asia, 6698765.
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., Nenadic, G. (2018). Machine Learning Methods for Wind Turbine Condition Monitoring: A Review. Renewable Energy, 133, 620-635.
Suryadarma, E.H.E., Ai, T.J. & Nugraha, B.B. (2020). Predictive Maintenance of Cooling System in Casting Process by Data SCADA. International Journal of Advanced Science and Technology, 29, 3555-3561.
Tautz-Weinert, J. & Watson, S. (2017). Using SCADA data for wind turbine condition monitoring - A review. IET Renewable Power Generation, 11(4), 382-394.
Tinham, B. (2008). Peak performance. Plant engineer London, 52(MAY/JUNE), 19-21.
Toporek, D. & Hutchings, A. (2011). Making fault data and non-SCADA data accessible for predictive analysis in data historians. IEEE Power and Energy Society General Meeting, 6039731.
UN (2008). International standard industrial classification of all economic activities revision 4. 4 ed. United Nations Publication, New York.
Wang, J., Liu, C., Zhu, M., Guo, P., Hu, Y. (2018). Sensor Data Based System-Level Anomaly Prediction for Smart Manufacturing. Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress, 158-165.
Waterbury, B. (2001). Is now the time for your plant to invest in automation and control? Control, 14(5), 40-47.
Zhao, W., Siegel, D. & Lee, J. S. L. (2013). An integrated framework of drivetrain degradation assessment and fault localization for offshore wind turbines. International Journal of Prognostics and Health Management, 4(46).
Zhao, Y., Li, D., Dong, A., Kang, D., Lv, Q., Shang, L. (2017). Fault prediction and diagnosis of wind turbine generators using SCADA data. Energies, 10(8), 1-17.
