Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Information Theory-Based Feature Selection: Minimum Distribution Similarity with Removed Redundancy Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Regression Methods for Detecting Anomalies in Flue Gas Desulphurization Installations in Coal-Fired Power Plants Based on Sensor Data

verfasst von : Marek Moleda, Alina Momot, Dariusz Mrozek

Erschienen in: Computational Science – ICCS 2020

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In the industrial world, the Internet of Things produces an enormous amount of data that we can use as a source for machine learning algorithms to optimize the production process. One area of application of this kind of advanced analytics is Predictive Maintenance, which involves early detection of faults based on existing metering. In this paper, we present the concept of a portable solution for a real-time condition monitoring system allowing for early detection of failures based on sensor data retrieved from SCADA systems. Although the data processed in systems, such as SCADA, are not initially intended for purposes other than controlling the production process, new technologies on the edge of big data and IoT remove these limitations and provide new possibilities of using advanced analytics. This paper shows how regression-based techniques can be adapted to fault detection based on actual process data from the oxygenating compressors in the flue gas desulphurization installation in a coal-fired power plant.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Deep Analytics for Management and Cybersecurity of the National Energy Grid
Nächstes Kapitel Autonomous Guided Vehicles for Smart Industries – The State-of-the-Art and Research Challenges
Literatur
1.
Afefy, I.H.: Reliability-centered maintenance methodology and application: a case study. Engineering 2(11), 863 (2010)CrossRef
2.
Awadallah, M.A., Morcos, M.M.: Application of ai tools in fault diagnosis of electrical machines and drives-an overview. IEEE Trans. Energy Convers. 18(2), 245–251 (2003)CrossRef
3.
Bachus, L., Custodio, A.: Know and Understand Centrifugal Pumps. Elsevier Science, Amsterdam (2003)
4.
Bevilacqua, M., Braglia, M.: The analytic hierarchy process applied to maintenance strategy selection. Reliab. Eng. Syst. Saf. 70(1), 71–83 (2000)CrossRef
5.
Boyer, S.A.: SCADA: Supervisory Control and Data Acquisition. International Society of Automation, Research Triangle Park (2009)
6.
Cococcioni, M., Lazzerini, B., Volpi, S.L.: Robust diagnosis of rolling element bearings based on classification techniques. IEEE Trans. Ind. Inform. 9(4), 2256–2263 (2012)CrossRef
7.
Feng, Y., Qiu, Y., Crabtree, C.J., Long, H., Tavner, P.J.: Monitoring wind turbine gearboxes. Wind Energy 16(5), 728–740 (2013)CrossRef
8.
Fortino, G., Savaglio, C., Zhou, M.: Toward opportunistic services for the industrial Internet of Things. In: 2017 13th IEEE Conference on Automation Science and Engineering (CASE), pp. 825–830. IEEE (2017)
9.
Freedman, D.A.: Statistical Models: Theory and Practice, Revised edn. Cambridge University Press, Cambridge (2009)CrossRef
10.
Konar, P., Chattopadhyay, P.: Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl. Soft Comput. 11(6), 4203–4211 (2011)CrossRef
11.
Malhi, A., Gao, R.X.: PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Measur. 53(6), 1517–1525 (2004)CrossRef
12.
Marscher, W.D., et al.: Avoiding failures in centrifugal pumps. In: Proceedings of the 19th International Pump Users Symposium. Texas A&M University. Turbomachinery Laboratories (2002)
13.
Moleda, M., Momot, A., Mrozek, D.: Predictive maintenance of boiler feed water pumps using SCADA data. Sensors 20(2), 571 (2020)CrossRef
14.
15.
Nouri, M., Fussell, B.K., Ziniti, B.L., Linder, E.: Real-time tool wear monitoring in milling using a cutting condition independent method. Int. J. Mach. Tools Manuf. 89, 1–13 (2015)CrossRef
16.
Qiu, Y., Chen, L., Feng, Y., Xu, Y.: An approach of quantifying gear fatigue life for wind turbine gearboxes using supervisory control and data acquisition data. Energies 10(8), 1084 (2017)CrossRef
17.
Qiu, Y., Feng, Y., Sun, J., Zhang, W., Infield, D.: Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data. IET Renew. Power Gener. 10(5), 661–668 (2016)CrossRef
18.
Qiu, Y., Feng, Y., Tavner, P., Richardson, P., Erdos, G., Chen, B.: Wind turbine SCADA alarm analysis for improving reliability. Wind Energy 15(8), 951–966 (2012)CrossRef
19.
Qu, J., Zhang, Z., Gong, T.: A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion. Neurocomputing 171, 837–853 (2016)CrossRef
20.
Rojas, A., Nandi, A.K.: Detection and classification of rolling-element bearing faults using support vector machines. In: 2005 IEEE Workshop on Machine Learning for Signal Processing, pp. 153–158. IEEE (2005)
21.
Susto, G.A., Schirru, A., Pampuri, S., McLoone, S., Beghi, A.: Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Ind. Inform. 11(3), 812–820 (2014)CrossRef
22.
Swanson, L.: Linking maintenance strategies to performance. Int. J. Prod. Econ. 70(3), 237–244 (2001)CrossRef
23.
Tautz-Weinert, J., Watson, S.J.: Using SCADA data for wind turbine condition monitoring-a review. IET Renew. Power Gener. 11(4), 382–394 (2016)CrossRef
24.
Tian, J., Morillo, C., Azarian, M.H., Pecht, M.: Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with k-nearest neighbor distance analysis. IEEE Trans. Ind. Electron. 63(3), 1793–1803 (2015)CrossRef
25.
Wu, J., Wu, C., Cao, S., Or, S.W., Deng, C., Shao, X.: Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines. IEEE Trans. Ind. Electron. 66(1), 529–539 (2018)CrossRef
26.
Yang, C., Liu, J., Zeng, Y., Xie, G.: Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model. Renew. Energy 133, 433–441 (2019)CrossRef
27.
Zhang, X., Xu, R., Kwan, C., Liang, S.Y., Xie, Q., Haynes, L.: An integrated approach to bearing fault diagnostics and prognostics. In: Proceedings of the 2005, American Control Conference, 2005, pp. 2750–2755. IEEE (2005)
Metadaten
Titel
Regression Methods for Detecting Anomalies in Flue Gas Desulphurization Installations in Coal-Fired Power Plants Based on Sensor Data
verfasst von
Marek Moleda
Alina Momot
Dariusz Mrozek
Copyright-Jahr
2020
Buch
Computational Science – ICCS 2020

Print ISBN: 978-3-030-50425-0

Electronic ISBN: 978-3-030-50426-7

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-50426-7_24