Genetic Algorithms for Artificial Neural Net-based Condition Monitoring System Design for Rotating Mechanical Systems

  • Abhinav Saxena
  • Ashraf Saad
Part of the Advances in Soft Computing book series (AINSC, volume 34)

Abstract

We present the results of our investigation into the use of Genetic Algorithms (GA) for identifying near optimal design parameters of Diagnostic Systems that are based on Artificial Neural Networks (ANNs) for condition monitoring of mechanical systems. ANNs have been widely used for health diagnosis of mechanical bearing using features extracted from vibration and acoustic emission signals. However, different sensors and the corresponding features exhibit varied response to different faults. Moreover, a number of different features can be used as inputs to a classifier ANN. Identification of the most useful features is important for an efficient classification as opposed to using all features from all channels, leading to very high computational cost and is, consequently, not desirable. Furthermore, determining the ANN structure is a fundamental design issue and can be critical for the classification performance. We show that GA can be used to select a smaller subset of features that together form a genetically fit family for successful fault identification and classification tasks. At the same time, an appropriate structure of the ANN, in terms of the number of nodes in the hidden layer, can be determined, resulting in improved performance.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Billington S A (1997), “Sensor and Machine Condition Effects in Roller Bearing Diagnostics”, Master’s Thesis, Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta.Google Scholar
  2. Balakrishnan K, Honavar V (1995), “Evolutionary Design of Neural Architecture – A Preliminary Taxonomy and guide to Literature”, Artificial Intelligence Research group, Iowa State University, CS Technical Report #95–01.Google Scholar
  3. Drakos N, “Genetic Algorithms as a Computational Tool for Design” http://www.cs.unr.edu/~sushil/papers/thesis/thesishtml/thesishtml.htmlGoogle Scholar
  4. Dallal G E (2004), “The Little Handbook of Statistical Practice”, http://www.tufts.edu/~gdallal/LHSP.HTM.Google Scholar
  5. Duda R O, Hart P E, Stork D G (2001), Pattern Classification, Second Edition, Wiley-Interscience Publications.Google Scholar
  6. Edwards D, Brown K, Taylor N(2002), “An Evolutionary Method for the Design of Generic Neural Networks”, CEC ′02. Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1769–1774.Google Scholar
  7. Filho E F M, de Carvalho A (1997), “Evolutionary Design of MLP Neural Network Architectures”, Proceedings of IVth Brazilian Symposium on Neural Networks, pp. 58 – 65Google Scholar
  8. Goldberg D E (1989), Genetic Algorithm in Search, Optimization and Machine Learning, Addison Wesley Publishing CompanyGoogle Scholar
  9. Holland J (1975), Adaptation In Natural and Artificial Systems. The University of Michigan Press, Ann ArborGoogle Scholar
  10. Jack L B, Nandi A K (2000), “Genetic Algorithms for Feature Selection in Machine Condition Monitoring With vibration Signals”, IEE Proceedings, Image Signal Processing, Vol. 147(3), June, pp 205–212.CrossRefGoogle Scholar
  11. Lawrence S, Giles C L, Tsoi A C (1997), “Lessons in Neural Network Training: Overfitting May be Harder than Expected”, Proceedings of the Fourth National Conference on Artificial Intelligence, AAAI-97, pp 540–545.Google Scholar
  12. Miguel ÁCarreira-Perpiñán (1996), A Review of Dimension Reduction Techniques (1997), Dept. of Computer Science, University of Sheffield, technical report CS-96-09.Google Scholar
  13. Riedmiller M, Braun H (1993), “A direct adaptive method for faster backpropagation learning: the RPROP algorithm”, IEEE International Conference on Neural Networks, vol. 1, pp. 586–591.Google Scholar
  14. Samanta B (2004a), “Gear Fault Detection Using Artificial Neural Networks and Support Vector Machines with Genetic Algorithms”, Mechanical Systems and Signal Processing, Vol. 18, pp. 625–644.CrossRefGoogle Scholar
  15. Samanta B (2004b), “Artificial Neural Networks and Genetic Algorithms for Gear Fault Detection”, Mechanical Systems and Signal Processing (Article in press).Google Scholar
  16. Shiroishi J, LiY, Liang S, Kurfess T, Danyluk S (1997), “Bearing Condition Diagnostics via Vibration & Acoustic Emission Measurements”, Mechanical Systems and Signal Processing, 11(5Google Scholar
  17. Sunghwan S, Dagli C H (2003), Proceedings of the International Joint Conference on Neural Networks, vol. 4, pp. 3218 –3222.Google Scholar
  18. Tang K S, Man K F, Kwong S, He Q (1996), “Genetic Algorithms and Their Applications”, IEEE Signal Processing Magazine (11), pp 21–37Google Scholar
  19. Vlachos M, Domeniconi C, Gunopulos G, Kollios G (2002), “Non-Linear Dimensionality Reduction Techniques for Classification and Visualization”, Proceedings of 8th SIGKDD Edmonton, Canada.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Abhinav Saxena
    • 1
  • Ashraf Saad
    • 2
  1. 1.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Associate Professor, School of Electrical and Computer EngineeringGeorgia Institute of TechnologySavannahUSA

Personalised recommendations