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 language | English |
|---|---|
| Title of host publication | Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 |
| Pages | 4812-4818 |
| Number of pages | 7 |
| ISBN (Electronic) | 978-1-4799-0652-9 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom Duration: 13 Oct 2013 → 16 Oct 2013 |
Conference
| Conference | 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 |
|---|---|
| Country | United Kingdom |
| City | Manchester |
| Period | 13/10/13 → 16/10/13 |
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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
Cite this
Automated sound signalling device quality assurance tool for embedded industrial control applications. / Maniak, Tomasz; Iqbal, Rahat; Doctor, Faiyaz; Jayne, Chrisina.
Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. 2013. p. 4812-4818 6722574.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding
}
TY - GEN
T1 - Automated sound signalling device quality assurance tool for embedded industrial control applications
AU - Maniak, Tomasz
AU - Iqbal, Rahat
AU - Doctor, Faiyaz
AU - Jayne, Chrisina
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Audio signal processing
KW - Embedded systems
KW - Environmental sound recognition
KW - Feed- forward back propagation
KW - Mel-scale frequency cepstum coefficients
KW - Non-speech sound recognition
U2 - 10.1109/SMC.2013.819
DO - 10.1109/SMC.2013.819
M3 - Conference proceeding
SN - 9780769551548
SP - 4812
EP - 4818
BT - Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
ER -