<p>Motor fault detection and classification are critical tasks in industrial applications, where sound signals are commonly employed for fault diagnosis. 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.</p>