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This study aims to classify motor faults using a cochlear transform and a self-organized DarkNet-based model to achieve high detection performance. A motor fault sound dataset comprising 3727 sound signals across five categories was collected. A novel approach based on cochlear transform and a self-organized, pretrained convolutional neural network is proposed. In this approach: (i) each sound signal is converted into an image using the cochlear transform; (ii) three feature vectors are extracted using pretrained DarkNet19 and DarkNet53 architectures; (iii) the top 500 features from each vector are selected using the Chi-square (Chi2) selector; and (iv) the selected 500-dimensional feature vectors are classified using a support vector machine (SVM) with 10-fold cross-validation. This pipeline represents a self-organized deep feature engineering model for motor fault classification. The primary goal of the proposed model is to maximize classification accuracy. The model achieved accuracies of 99.87%, 99.92%, and 99.70% using the three generated feature vectors, with the highest classification accuracy of 99.92% being selected. The achieved classification accuracy of 99.92% demonstrates the effectiveness and reliability of the proposed method for motor fault classification.<\/jats:p>","DOI":"10.1007\/s11042-025-20929-5","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T06:40:44Z","timestamp":1748414444000},"page":"45017-45040","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cochlear transform and self-organized DarkNet based automated motor fault classification using sound signals"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1720-1285","authenticated-orcid":false,"given":"Gullu","family":"Boztas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,28]]},"reference":[{"issue":"1","key":"20929_CR1","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1109\/TIA.2021.3131296","volume":"58","author":"KN Gyftakis","year":"2022","unstructured":"Gyftakis KN (2022) A comparative investigation of interturn faults in induction motors suggesting a novel transient diagnostic method based on the goerges phenomenon. 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