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

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.