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Local Termination Criterion for Impulsive Component Detection Using Progressive Genetic Algorithm

  • Jacek WodeckiEmail author
  • Anna Michalak
  • Agnieszka Wyłomańska
  • Radosław Zimroz
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)

Abstract

A problem of local damage detection for condition monitoring based on vibration data can be approached from many different angles. One of the most common ways is selective filtration of the vibration signal. There are many techniques allowing to construct digital filter for particular input data (e.g. spectral selectors). In previous articles authors proposed a technique called Progressive Genetic Algorithm (PGA) to optimally design digital filter for a given data set using no prior assumptions. It uses kurtosis as fitness function and local linear fit of fitness function progression vector as a global termination criterion (GTC), but local termination criterion (LTC) was defined as simple stall limit of fitness value. In this paper authors propose a new quantile-based way to terminate PGA locally for faster convergence. Initial testing phase shows that for comparable quality of obtained result, individual epochs terminate significantly faster without sacrificing the progress of local convergence. It results in more efficient optimization and faster global convergence which reduces the overall execution time of the program for about the order of magnitude.

Keywords

Genetic algorithm Local damage detection Vibration signal Statistical analysis 

References

  1. 1.
    Bartelmus W, Zimroz R (2009) A new feature for monitoring the condition of gearboxes in non-stationary operating conditions. Mech Syst Signal Process 23(5):1528–1534CrossRefGoogle Scholar
  2. 2.
    Obuchowski J, Wyłomańska A, Zimroz R (2014) Selection of informative frequency band in local damage detection in rotating machinery. Mech Syst Signal Process 48(1):138–152CrossRefGoogle Scholar
  3. 3.
    Obuchowski J, Wylomańska A, Zimroz R (2014) Recent developments in vibration based diagnostics of gear and bearings used in belt conveyors. Appl Mech Mater 683:171–176CrossRefGoogle Scholar
  4. 4.
    Wyłomańska A, Zimroz R, Janczura J, Obuchowski J (2016) Impulsive noise cancellation method for copper ore crusher vibration signals enhancement. IEEE Trans Ind Electron 63(9):5612–5621CrossRefGoogle Scholar
  5. 5.
    Żak G, Wyłomańska A, Zimroz R (2016) Data-driven vibration signal filtering procedure based on the \(\alpha \)-stable distribution. J Vibroeng 18(2):826–837Google Scholar
  6. 6.
    Makowski R, Zimroz R (2014) New techniques of local damage detection in machinery based on stochastic modelling using adaptive Schur filter. Appl Acoust 77:130–137CrossRefGoogle Scholar
  7. 7.
    Makowski R, Zimroz R (2013) A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings. Mech Syst Signal Process 38(1):65–77CrossRefGoogle Scholar
  8. 8.
    Wodecki J, Kruczek P, Wyłomańska A, Bartkowiak A, Zimroz R (2017) Novel method of informative frequency band selection for vibration signal using nonnegative matrix factorization of short-time fourier transform. In: 2017 IEEE 11th international symposium on diagnostics for electrical machines, power electronics and drives (SDEMPED). IEEE, pp 129–133Google Scholar
  9. 9.
    Wyłomańska A, Żak G, Kruczek P, Zimroz R (2017) Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection. Appl Acoust 128:14–22CrossRefGoogle Scholar
  10. 10.
    Wodecki J, Michalak A, Zimroz R (2018) Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings. Mech Syst Signal Process 102:102–116CrossRefGoogle Scholar
  11. 11.
    Nilsson M, Dahl M, Claesson I (2003) Digital filter design of IIR filters using real valued genetic algorithm. In: WSEASGoogle Scholar
  12. 12.
    Lee A, Ahmadi M, Jullien GA, Miller WC, Lashkari RS (1999) Digital filter design using genetic algorithm. In: 1998 IEEE symposium on advances in digital filtering and signal processing, symposium proceedings (Cat. No. 98EX185), pp 34–38Google Scholar
  13. 13.
    Sabbir U, Antoniou A (2006) Design of digital filters using genetic algorithms. In: 6th IEEE international symposium on signal processing and information technology, August 2006Google Scholar
  14. 14.
    Langford E (2006) Quartiles in elementary statistics. J Stat Educ 14(3):1–27CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jacek Wodecki
    • 2
    Email author
  • Anna Michalak
    • 1
  • Agnieszka Wyłomańska
    • 1
  • Radosław Zimroz
    • 2
  1. 1.Research and Development CentreKGHM Cuprum Ltd.WroclawPoland
  2. 2.Faculty of Geoengineering, Mining and Geology, Diagnostics and Vibro-Acoustic Science LaboratoryWroclaw University of Science and TechnologyWroclawPoland

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