A CNN Based Ensemble Approach for Malfunction Detection from Machine Sounds

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Tayfun Özçay
Nermin Yalçı
Semra Erpolat Taşabat
Mehmet Ali Varol
Berk Kayı
Melih Yılmaz Öğütcen
Berk Öztürk


Together with the meaning and essence of data for the company nowadays; The variety of data also differs. One of these differentiating data types is sound. Borusan Makina ve Güç Sistemleri A.Ş. the data obtained from the Caterpillar construction machines of. The machine sound gives clues about many malfunctions. Artificial intelligence systems of the heard sound will be integrated into business processes. Every tone can be converted. With this, the properties and estimates of the sound grids are used. In this direction; While the incident is getting in the way of his business, an unfortunate project occurs with a similar visitor. The traditional will use a meaningful method by listening to the producer's sound and technology and innovation to develop easy blueprints of decisions that cannot be diverted to sound data. Thanks to the real-time model with short-term audio recording, it is instantly predicted whether there is a problem in the machine. Free from personal and technical comments; By examining the patterns of sound waves, it is aimed to be made without cancellation.


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Akca, E., Özçay, T., Dinç, Y., Yalçı, N., Erpolat Taşabat, S., Varol, M. A., Kayı, B., Öğütcen, M. Y., & Öztürk, B. (2022). A CNN Based Ensemble Approach for Malfunction Detection from Machine Sounds. The European Journal of Research and Development, 2(2), 411–420. https://doi.org/10.56038/ejrnd.v2i2.37


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