Morphological dictionary learning based sparse classification for small electric motor state recognition under unbalanced samples

Accurately recognizing sound states in the production line of small electric motors is of great importance for manufacturers to carry out quick repairs and ensure high quality deliveries. Since the number of normal samples is much larger than the number of abnormal samples in practice, resulting in unbalanced data, which poses huge challenges to traditional detection methods. To overcome these difficulties, this study presents a morphological dictionary learning-based sparse classification (MDL-SC) combined with audio data augmentation method for small electric motor state recognition under unbalanced samples. Firstly, audio data augmentation methods such as adding background noise, pitch shifting, time stretching and combined augmentation are investigated for augmenting the number and diversity of samples. Secondly, morphological dictionary learning is proposed for characterizing transient sounds of small electric motors and enhancing the discriminative feature learning capability of the dictionary. Finally, the minimum reconstruction error strategy is relied upon to establish automatic recognition of small electric motor states. Three small motor datasets with unbalanced ratios are established in the experiments to verify the effectiveness of the proposed MDL-SC, which has higher recognition accuracy under unbalanced conditions compared with traditional dictionary learning based sparse classification (DL-SC), k-nearest neighbors, support vector machines and convolutional neural networks. This study can provide some theoretical implications for the later development of online detection of small electric motors or other types of electric motors.