Development of remote diagnostic monitoring system for pumping equipment with open architecture
Abstract
Keywords
Full Text:
PDFReferences
Chen, W. Intelligent manufacturing production line data monitoring system for industrial internet of things. Computer Communications, 2020, vol. 151, pp. 31-41. DOI: 10.1016/j.comcom.2019.12.035.
Pech, M., Vrchota, J., & Bednář, J. Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors, 2021, vol. 21, no. 4, article no. 1470. DOI: 10.3390/s21041470.
Golmakani, H. R., & Fattahipour, F. Optimal replacement policy and inspection interval for condition-based maintenance. International Journal of Production Research, 2010, vol. 49, no. 17, pp. 5153–5167. DOI: 10.1080/00207543.2010.505935.
Wang, Y., Deng, C., Wu, J., Wang, Y., & Xiong, Y. A corrective maintenance scheme for engineering equipment. Engineering Failure Analysis, 2014, vol. 36, pp. 269-283. DOI: 10.1016/j.engfailanal.2013.10.006.
Jardine, A.K.S., Lin, D., & Banjevic, D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 2006, vol. 20, no. 7, pp. 1483-1510. DOI: 10.1016/j.ymssp.2005.09.012.
Stenström, C., Norrbin, P., Parida, A., & Kumar, U. Preventive and corrective maintenance – cost comparison and cost–benefit analysis. Structure and Infrastructure Engineering, 2015, vol. 12, no. 5, pp. 603–617. DOI: 10.1080/15732479.2015.1032983.
Randall, R. B. Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications. John Wiley & Sons Ltd., 2011. 289 p.
Ross-Pinnock, D., & Maropoulos, P.G. Review of industrial temperature measurement technologies and research priorities for the thermal characterisation of the factories of the future. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2016, vol. 230, no. 5, pp. 793-806. DOI: 10.1177/0954405414567929.
Koshekov, K.T., Klikushin, Y.N., Kobenko, V.Y., Sof”ina, N.N., Savostin, A.A., & Kashevkin, A.A. Testing a pump unit by identification measurements of vibration signals. Russian Journal of Nondestructive Testing, 2016, vol. 52, pp. 280–286. DOI: 10.1134/S1061830916050041.
Albraik, A., Althobiani, F., Gu, F., & Ball, A. Diagnosis of Centrifugal Pump Faults Using Vibration Methods. 25th International Congress on Condition Monitoring and Diagnostic Engineering 2012. COMADEM 2012. Journal of Physics: Conference Series. IOP Publishing, 2012, vol. 364, article no. 012139. DOI: 10.1088/1742-6596/364/1/012139.
Short, M., & Twiddle, J. An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment. Sensors. 2019, vol. 19, no. 17, article no. 3781. DOI: 10.3390/s19173781.
Kulik, A.S. Rational Control of the Operability of Autonomous Aircrafts. Part 1. Journal of Automation and Information Sciences, 2017, vol. 49, no. 5, pp. 1-15. DOI: 10.1615/JAutomatInfScien.v49.i5.10.
Orrù, P.F., Zoccheddu, A., Sassu, L., Mattia, C., Cozza, R., & Arena, S. Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry. Sustainability, 2020, vol. 12, no. 11, article no. 4776. DOI: 10.3390/su12114776.
Azadeh, A., Saberi, M., Kazem, A., Ebrahimipour, V., Nourmohammadzadeh, A., & Saberi, Z. A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization. Applied Soft Computing, 2013, vol. 13, no. 3, pp. 1478-1485. DOI: 10.1016/j.asoc.2012.06.020.
Zhao, W., Egusquiza, M., Valero, C., Valentín D., Presas, A., & Egusquiza, E. On the use of artificial neural networks for condition monitoring of pump-turbines with extended operation, Measurement, 2020, vol. 163, article no. 107952. DOI: 10.1016/j.measurement.2020.107952.
Irfan, M.B., Ahmed, U., & Siddiqui, T. Predictive Maintenance Strategies for Oil and Gas Facilities Using AI-Based Vibration Monitoring. Proceedings of the International Field Exploration and Development Conference 2023. Springer series in geomechanics and geoengineering. Springer, 2024, pp. 305-312. DOI: 10.1007/978-981-97-0268-8_25.
Gonçalves, J.P.S., Fruett, F., Dalfré Filho, J.G., & Giesbrecht, M. Faults detection and classification in a centrifugal pump from vibration data using markov parameters. Mechanical Systems and Signal Processing, 2021, vol. 158, article no. 107694. DOI: 10.1016/j.ymssp.2021.107694.
Kerkeni, R., Khlif, S., Mhalla, A., & Bouzrara, K. Digital Twin applied to Predictive Maintenance for Industry 4.0. Journal of nondestructive evaluation, diagnostics and prognostics of engineering systems, 2024, vol. 7, no. 4, article no. 041008. DOI: 10.1115/1.4065875.
Liu, Z., Blasch, E., Liao, M., Yang, C., Tsukada, K., & Meyendorf, N. Digital twin for predictive maintenance. SPIE Smart Structures + Nondestructive Evaluation 2023. Proceedings SPIE. SPIE, 2023, article no. 1248907. DOI: 10.1117/12.2660270.
Hallaji, S.M., Fang, Y. & Winfrey, B. A digital twin framework for enhancing predictive maintenance of pumps in wastewater treatment plants. 38th International Symposium on Automation and Robotics in Construction 2021. ISARC 2021. Proceedings of the International Symposium on Automation and Robotics in Construction. The International Association for Automation and Robotics in Construction, 2021, pp. 88-93. DOI: 10.22260/ISARC2021/0014.
Bonilla, C.A., Zanfei, A., Brentan, B., Montalvo, I., & Izquierdo, J. A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation. Water, 2022, vol. 14, no.4, article no. 514. DOI: 10.3390/w14040514.
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 2017, vol. 8, article no. 31. DOI: 10.36001/ijphm.2017.v8i3.2667.
Jamshed, M.A., Ali, K., Abbasi Q.H., Imran M.A., & Ur-Rehman, M. Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review. IEEE Sensors Journal, 2022, vol. 22, no. 6, pp. 5482-5494. DOI: 10.1109/JSEN.2022.3148128.
Asghari, P., Rahmani, A.M., & Javadi, H.H.S. Internet of Things applications: A systematic review, Computer Networks, 2019, vol. 148, pp. 241-261. DOI: 10.1016/j.comnet.2018.12.008.
Chen, L., Wei, L., Wang, Y., Wang, J., & Li, W. Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors. Sensors, 2022, vol. 22, no. 6, article no. 2106. DOI: 10.3390/s22062106.
Mallioris, P., Diamantis, E., Bialas, C., & Bechtsis, D. Predictive maintenance framework for assessing health state of centrifugal pumps. IAES International Journal of Artificial Intelligence, 2024, vol. 13, no. 1, pp. 850-862. DOI: 10.11591/ijai.v13.i1.pp850-862.
ISO 10816-7:2009. Mechanical vibration – Evaluation of machine vibration by measurements on non-rotating parts. Part 7: Rotodynamic pumps for industrial applications, including measurements on rotating shafts. Geneva, International Organization for Standardization, 2009. 17 p.
Holton, T. Digital Signal Processing: Principles and Applications. Cambridge: Cambridge University Press, 2021. 1058 p.
Mele, J., Guzzomi, A., & Pan, J. Correlation of centrifugal pump vibration to unsteady flow under variable motor speed. Mechanics & Industry, 2014, vol. 15 no. 6, pp. 525-534. DOI:10.1051/meca/2014051.
Kondhalkar, G.E., & Diwakar, G. Crest Factor Measurement by Experimental Vibration Analysis for Preventive Maintenance of Bearing. ICRRM 2019 – System Reliability, Quality Control, Safety, Maintenance and Management. Springer, 2020, pp. 133-138. DOI: 10.1007/978-981-13-8507-0_21.
Li, C., Sánchez, R-V., Zurita, G., Cerrada, M., & Cabrera, D. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning. Sensors, 2016, vol. 16, no.6, article no. 895. DOI: 10.3390/s16060895.
Jain, P., & Bhosle, S.P. Analysis of vibration signals caused by ball bearing defects using time-domain statistical indicators. International Journal of Advanced Technology and Engineering Exploration, 2022, vol 9, no. 90, pp. 700-715. DOI: 10.19101/IJATEE.2021.875416.
Savostin, A.A., Tuleshov, A.K., & Savostina, G.V. Razrabotka sistemy diagnosticheskogo monitoringa nasosnogo oborudovaniya s primeneniem tekhnologii besprovodnoy peredachi dannykh [Development of a system for diagnostic monitoring of pumping equipment using wireless data transmission technology]. Bulletin of the National Engineering Academy of the Republic of Kazakhstan, 2024, vol. 3, pp. 178-189. DOI:10.47533/2024.1606-146X.60. (In Russian).
Jolliffe, I.T. Principal Component Analysis. Springer, 2002. 487 p.
Hassan, I.U., Panduru, K., & Walsh, J. An In-Depth Study of Vibration Sensors for Condition Monitoring. Sensors, 2024, vol. 24, no. 3, article no. 740. DOI: 10.3390/s24030740.
Bonilla, V., Campoverde, B., & Yoo, S.G. A Systematic Literature Review of LoRaWAN: Sensors and Applications. Sensors, 2023, vol. 23, no. 20, article no. 8440. DOI: 10.3390/s23208440.
Borkar, S.R. Long-term evolution for machines (LTE-M), Editor(s): Bharat S. Chaudhari, Marco Zennaro, LPWAN Technologies for IoT and M2M Applications, Academic Press, 2020, pp. 145-166. DOI: 10.1016/B978-0-12-818880-4.00007-7.
Putra, B.D., Munadi, R., Walidainy, H., Arif S.T. & Putra, A.T. Smart University Development Challenges using Lora or Sigfox technology: A Systematic Literature Review. 2022 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE), Riau, Indonesia, 2022, pp. 36-40. DOI: 10.1109/FORTEI-ICEE57243.2022.9972910.
Telegraf. Available at: https://www.influxdata.com/time-series-platform/telegraf/ (аccessed 19.10.2024).
InfluxDB. Available at: https://www.influxdata.com/ (аccessed 19.08.2024).
Grafana. Available at: https://grafana.com/ (аccessed 19.10.2024).
Docker. Available at: https://www.docker.com/ (аccessed 19.10.2024).
WISE-2410 LoRaWAN Smart Vibration Sensor. Available at: https://www.advantech.com/en/products/b7e2306f-d561-4ca9-b0e3-33f7057e185f/wise-2410/mod_25018dc7-355c-40b4-bf9b-c93f6c73f1a0 (аccessed 19.08.2024).
Industrial LoRaWAN Gateway WISE-6610 V2. Available at: https://www.advantech.com/ru-ru/products/23ed4776-1633-4901-a776-8532a23ea8b4/wise-6610-v2/mod_95986887-f989-40d0-9d47-5c8cf8b83cbd. 19 avgusta 2024 (аccessed 19.10.2024).
Pump vibration standards guidelines. European association of pump manufacturers. First edition - 15 July 2013. Available at: https://www.europump.net/files/Publications/Guides/Guidelines%200n%20Pump%20Vibration%20First%20edition%20Final%20July%202013.pdf (аccessed 19.10.2024).
DOI: https://doi.org/10.32620/reks.2024.4.16
Refbacks
- There are currently no refbacks.