Automated sound signalling device quality assurance tool for embedded industrial control applications

Tomasz Maniak, Rahat Iqbal, Faiyaz Doctor, Chrisina Jayne

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding

    5 Citations (Scopus)

    Abstract

    This paper presents a novel system for automatic detection and recognition of faulty audio signaling devices as part of an automated industrial manufacturing process. The system uses historical data labeled by human experts in detecting faulty signaling devices to train an artificial neural network based classifier for modeling their decision making process. The neural network is implemented on a real time embedded microcontroller which can be more efficiently incorporated into an automated production line eliminating the need for a manual inspection within the manufacturing process. We present real world experiments based on data pertaining to the production and manufacture of audio signaling components used in car instrument clusters. Our results show that the proposed expert system is able to successfully classify faulty audio signaling devices to a high degree of accuracy. The results can be generalized to other signaling devices where an output signal is represented by a complex and changing frequency spectrum even with significant environmental noise.

    Original languageEnglish
    Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
    Pages4812-4818
    Number of pages7
    ISBN (Electronic)978-1-4799-0652-9
    DOIs
    Publication statusPublished - 2013
    Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
    Duration: 13 Oct 201316 Oct 2013

    Conference

    Conference2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
    CountryUnited Kingdom
    CityManchester
    Period13/10/1316/10/13

    Keywords

    • Artificial neural networks
    • Audio signal processing
    • Embedded systems
    • Environmental sound recognition
    • Feed- forward back propagation
    • Mel-scale frequency cepstum coefficients
    • Non-speech sound recognition

    ASJC Scopus subject areas

    • Human-Computer Interaction

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