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:
Extending the Delaunay Triangulation Based Density Measurement to Many-Objective Optimization Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN

Authors : Dileep Kumar Appana, Md. Rashedul Islam, Jong-Myon Kim

Published in: Artificial Life and Computational Intelligence

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

The k-nearest neighbor (k-NN) method is a simple and highly effective classifier, but the classification accuracy of k-NN is degraded and becomes highly sensitive to the neighborhood size k in multi-classification problems, where the density of data samples varies across different classes. This is mainly due to the method using only a distance-based measure of similarity between different samples. In this paper, we propose a density-weighted distance similarity metric, which considers the relative densities of samples in addition to the distances between samples to improve the classification accuracy of standard k-NN. The performance of the proposed k-NN approach is not affected by the neighborhood size k. Experimental results show that the proposed approach yields better classification accuracy than traditional k-NN for fault diagnosis of rolling element bearings.

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 A Hybrid Feature Selection Scheme Based on Local Compactness and Global Separability for Improving Roller Bearing Diagnostic Performance
next chapter Towards Solving TSPN with Arbitrary Neighborhoods: A Hybrid Solution
Literature
1.
Dong, S., Shirong, Y., Baoping, T., Chen, L., Tianhong, L.: Bearing degradation process prediction based on the support vector machine and Markov model. Shock Vib. 2014, 15 p. (2014)
2.
Thorsen, O., Magnus, D.: Failure identification and analysis for high voltage induction motors in petrochemical industry. In: 1998 IEEE Industry Applications Conference, Thirty-Third IAS Annual Meeting, vol. 1, pp. 291–298 (1998)
3.
Hansen, D., Olsson, A.H.: ISO standard 13373-2: 2005: condition monitoring and diagnostics of machines–vibration condition monitoring–part 2: processing, analysis and presentation of vibration data. International Standards Organization (2009)
4.
Andrew, J.K.S., Daming, L., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 7, 1483–1510 (2006)
5.
Xiao, X., Huafeng, D.: Enhancement of K-nearest neighbor algorithm based on weighted entropy of attribute value. In: 5th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 1261–1264 (2012)
6.
Wu, Y., Ianakiev, K., Venu, G.: Improved k-nearest neighbor classification. Pattern Recogn. Lett. 35(10), 2311–2318 (2002)CrossRefMATH
7.
Baoli, L., Yu, S., Lu, Q.: An improved k-nearest neighbor algorithm for text categorization. In: Proceedings of the 20th International Conference on Computer Processing of Oriental Languages, Shenyang, China (2003)
8.
Jiang, L., Zhihua, C., Dianhong, W., Siwei, J.: Survey of improving K-nearest-neighbor for classification. In: 4th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 679–683 (2007)
9.
Hand, D.J., Vinciotti, V.: Choosing k for two-class nearest neighbor classifiers with unbalanced classes. Pattern Recogn. Lett. 24(9), 1555–1562 (2003)CrossRefMATH
10.
Shiliang, S., Huang, R.: An adaptive k-nearest neighbor algorithm. In: IEEE 7th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 1, pp. 91–94 (2010)
11.
Jia, W., Cai, Z., Gao, Z.: Dynamic K-nearest-neighbor with distance and attribute weighted for classification. In: IEEE International Conference on Electronics and Information Engineering, vol. 1, pp. V1–356 (2010)
12.
Wesam, A., Murtaja, M.: Finding within cluster dense regions using distance based technique. Int. J. Intell. Syst. Appl. 2, 42 (2012)
13.
Lee, J., Qiu, H., Yu, G., Lin, J.: Rexnord Technical Services, Bearing Data Set, IMS, University of Cincinnati, NASA Ames Prognostics Data Repository (2007)
14.
Kim, C.H., Uddin, S., Islam, R., Kim, J.M.: Many-core accelerated local outlier factor based classifier in bearing fault diagnosis. In: IEEE 18th International Conference on Computer and Information Technology, pp. 445–449 (2015)
15.
Xia, Z., Shixiong, X., Wan, L., Cai, S.: Spectral regression based fault feature extraction for bearing accelerometer sensor signals. Sensors 10, 13694–13719 (2012)CrossRef
16.
Yaqub, M., Iqbal, G., Joarder, K.: Inchoate fault detection framework: adaptive selection of wavelet nodes and cumulant orders. IEEE Trans. Instrum. Meas. 3, 685–695 (2012)CrossRef
17.
Li, B., Lie, Z.P., Liu, D., Mi, S., Ren, G., Tian, H.: Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization. J. Sound Vib. 10(330), 2388–2399 (2011)CrossRef
18.
Kang, M., Islam, R., Kim, J., Kim, J.M., Pecht, M.: A hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics. IEEE Trans. Industr. Electron. 63(5), 3299–3310 (2016)CrossRef
Metadata
Title
Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN
Authors
Dileep Kumar Appana
Md. Rashedul Islam
Jong-Myon Kim
Copyright Year
2017
Book
Artificial Life and Computational Intelligence

Print ISBN: 978-3-319-51690-5

Electronic ISBN: 978-3-319-51691-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51691-2_17

Premium Partner

    Image Credits