Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Flow Analysis and Its Applications for Equipment Design Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Outlier Detection in Predictive Analytics for Energy Equipment

Authors : Alexander Andryushin, Ivan Shcherbatov, Nina Dolbikova, Anna Kuznetsova, Grigory Tsurikov

Published in: Cyber-Physical Systems: Advances in Design & Modelling

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The method of data preprocessing used to predict the technical condition of power equipment is described. Preprocessing implemented using neural networks allows us to identify and eliminate outliers in the investigated data. An example illustrating the proposed method of processing big data using bagged trees algorithm, support vector machines and artificial neural networks is shown.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Regularization Methods for the Stable Identification of Probabilistic Characteristics of Stochastic Structures
next chapter Ontology-Based Model of User Activity Data for Cyber-Physical Systems
Literature
1.
Protalinsky, O., Khanova, A., Shcherbatov, I. Simulation of power assets management process. In: Studies in Systems, Decision and Control (2019)
2.
Protalinsky, O.M., Shcherbatov, I.A., Stepanov, P.V.: Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies. J Phys Conf Ser (2017)
3.
4.
Bi, Z.M., Cochran, D.S.: Big data analytics with applications. J. Manag. Anal. 1(4), 249–265 (2014)
5.
Kumaraguru S., Kulvatunyou B., Morris K.C.: Integrating real-time analytics and continuous performance management in smart manufacturing systems. In: Proceedings of the IFIP Advances in Information and Communication Technology, pp. 175–182 (2014)CrossRef
6.
Chidambaram, V., Evans, H., Etheredge, K.: Big data: is the energy industry starting to see real applications? Supply Chain Manag. Rev. 12, 62–64 (2015)
7.
8.
Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inf. Integ. 6, 1–10 (2017)CrossRef
9.
Shin, S.J., Meilanitasari, P.: Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation. International J. Adv. Manuf. Technol., 1–42 (2018)
10.
Peres, R.S., Rocha, A.D., Leitao, P., Barata, J.: IDARTS—towards intelligent data analysis and real-time supervision for Industry 4.0. Comput. Ind., 1–12 (2018)
11.
Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: methods and applications. J. Manuf. Syst. 48, 144–156 (2018)CrossRef
12.
Rose, K., Eldridge, S., Chapin, L.: The internet of things: an overview. Internet Soc., 7 (2015)
13.
Shina, S.J., Wooa, J., Rachuri, S.: Predictive analytics model for power consumption in manufacturing. In: Proceedings in 21st CIRP Conference on Life Cycle Engineering, pp. 153–158 (2014)CrossRef
14.
Park, J.K., Kwon, B.K., Park, J.H., Kang, D.J.: Machine learning-based imaging system for surface defect inspection. Int. J. Precis. Eng. Manuf.-Green Technol. 3(3), 303–310 (2016)CrossRef
15.
16.
Johnson, T., Kwok, I., Ng, R.T.: Fast computation of 2-dimensional depth contours. In: Proceedings of the ACM KDD Conference, pp. 224–228, New York, NY, USA, 27–31 Aug 1998
17.
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104, Dallas, TX, USA, 16–18 May 2000. ACM, New York, NY, USA (2000)
18.
Marti, L., Sanchez-Pi, N., Molina, J.M., Bicharra Garcia, A.C.: Anomaly detection based on sensor data in petroleum industry applications. Sensors 15, 2774–2797 (2015)CrossRef
19.
Loureiro, A., Torgo, L., Soares, C.: Outlier detection using clustering methods: a data cleaning application. In: Malerba, D., May, M. (eds.) Proceedings of KDNet Symposium on Knowledge-based Systems for the Public Sector (2004)
20.
Upadhyaya, S., Singh, K.: Classification based outlier detection techniques. Int. J. Comput. Trends Technol. 3(2), 294–298 (2012)
21.
Ting, K.M.: An instance-weighting method to induce cost-sensitive trees. IEEE Trans. Knowl. Data Eng. 14, 659–665 (2002)CrossRef
22.
Weiss, G., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Intell. Res. 19, 315–354 (2003)CrossRef
23.
Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.: SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 281–288 (2009)CrossRef
24.
Wu, G., Chang, E.Y.: Class-boundary alignment for imbalanced dataset learning. In: Proceedings of the ICML Workshop on Learning from Imbalanced Data Sets (2003)
25.
Fedorov, E.E.: Artificial neural networks: monograph, 317 p. DVNZ DonNTU, Krasnoarmeysk (2016) (in Russian)
Metadata
Title
Outlier Detection in Predictive Analytics for Energy Equipment
Authors
Alexander Andryushin
Ivan Shcherbatov
Nina Dolbikova
Anna Kuznetsova
Grigory Tsurikov
Copyright Year
2020
Book
Cyber-Physical Systems: Advances in Design & Modelling

Print ISBN: 978-3-030-32578-7

Electronic ISBN: 978-3-030-32579-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-32579-4_15

Premium Partner

    Image Credits