Repository Copy of: CNN Based Ensemble Approach for Malfunction Detection from Machine Sounds
Key Points
To explore a CNN-based ensemble method for detecting malfunctions from audio signals of machinery.
Developed a convolutional neural network (CNN) model to analyze machine sounds.
Utilized ensemble techniques to improve detection accuracy.
Evaluated model performance on sound datasets.
Achieved improved accuracy in malfunction detection compared to traditional methods.
Demonstrated effectiveness of ensemble strategies in enhancing detection rates.
Showed potential applicability for real-time monitoring of machinery.
Abstract
This record is a repository-preserved copy of an article originally published in The European Journal of Research and Development by Orclever Science it is not the version of record, and Zenodo is not the publisher of this work. Version of Record (primary publication): https://doi.org/10.56038/ejrnd.v2i2.37 Publisher: Orclever Science & Research Group. Journal: The European Journal of Research and Development. For citation, please use the Crossref DOI and the journal citation above — not the Zenodo DOI.