Fault Detection and Prediction of Clocks and Timers Based on Computer Audition and Probabilistic Neural Networks

  • S. Y. Chen
  • C. Y. Yao
  • G. Xiao
  • Y. S. Ying
  • W. L. Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)

Abstract

This paper investigates the fault detection and prediction of rhythmically soniferous products, such as clocks, watches and timers. Such products with fault cannot work steadily or probably cause malfunction. The authors extend the concept of computer audition and establish an architectural model of product fault prediction system based on probabilistic neural networks. The system listens to the product sound by the multimedia technology and the sound features are extracted to detect and predict faults by the neural network. The paper analyzes the reasons of timer faults and the corresponding sound features. Experiments are made in the laboratory to demonstrate the proposed method. The technology is expected to apply in factories in coming years for realizing automatic product test and improving efficiency of product inspection.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rutkowski, L.: Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Trans. on Neural Networks. 15(4), 811–827 (2004)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Lin, W.-M., Lin, C.-H., Sun, Z.-C.: Adaptive multiple fault detection and alarm processing for loop system with probabilistic network. IEEE Trans. on Power Delivery. 19(1), 64–69 (2004)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Lennox, B., Rutherford, P.: Novel fault prediction technique using model degradation analysis. In: Proc. of the American Control Conference, vol. 5, pp. 3274–3278 (1995)Google Scholar
  4. 4.
    Ruan, Q., Yuan, B.: Status and trends of computer audio-visual information processing. Telecommunications Science 9(4), 23–30 (1993)Google Scholar
  5. 5.
    Lau, C., Widrow, B.: Special Issue on Neural Networks II: Mathematical Analysis: Implementations and Applications. Proc. IEEE 78(10) (1990)Google Scholar
  6. 6.
    Zhao, J.S., Chen, B.Z., Shen, J.Z.: A Neural Network Approach to the Dynamic Fault Diagnosis of Hydrocracking Process. In: Proc. of the Second Chinese World Congress on Intelligent Control and Intelligent Automation. Xian, China, June 1997, pp. 1892–1897 (1997)Google Scholar
  7. 7.
    Ganchev, T., Fakotakis, N., Tasoulis, D.K., Vrahatis, M.N.: Generalized locally recurrent probabilistic neural networks for text-independent speaker verification. In: IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 1, p. I-41-4 (2004)Google Scholar
  8. 8.
    Bolat, B., Kucuk, O.: Speeh/music classification by using statistical neural networks. In: IEEE 12th Signal Processing and Communications Applications Conference, pp. 227–229 (April 2004)Google Scholar
  9. 9.
    Comes, B., Kelemen, A.: Probabilistic neural network classification for microarraydata. In: Int. Joint Conf. on Neural Networks, vol. 3, pp. 1714–1717 (July 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • S. Y. Chen
    • 1
    • 2
  • C. Y. Yao
    • 1
  • G. Xiao
    • 1
  • Y. S. Ying
    • 1
  • W. L. Wang
    • 1
  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhouChina
  2. 2.National Laboratory of Pattern Recognition of Automation InstituteChinese Academy of SciencesChina

Personalised recommendations