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.
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Chen, S.Y., Yao, C.Y., Xiao, G., Ying, Y.S., Wang, W.L. (2005). Fault Detection and Prediction of Clocks and Timers Based on Computer Audition and Probabilistic Neural Networks. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_117
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DOI: https://doi.org/10.1007/11494669_117
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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