Hybrid data augmentation method for combined failure recognition in rotating machines

Rotating machines are frequently subject to a wide range of rough conditions, resulting in mechanical failures and performance degradation. Thus, it is important to apply proper failure detection and recognition techniques, such as machine learning algorithms, to prevent these issues early. In industrial environments, little data exists regarding failure conditions, which hinders the training stage of the classification algorithms responsible for classifying the failures. Therefore, this work proposes a hybrid method of data augmentation to increase the number of minority class instances in order to improve classifier performance. The approach combines the synthetic minority over-sampling and the additive white Gaussian noise techniques to create a set of artificial signals. The results show that the proposal is able to achieve better results than applying those techniques separately and also when using an undersampling strategy. For comparison purposes, four machine learning classification methods were analyzed alongside our data augmentation proposal, namely, support vector machines, K-nearest neighbors, random forest and stacked sparse autoencoder. The proposed hybrid data augmentation method associated with stacked sparse autoencoder outperformed the other models obtaining an accuracy of 100% and a processing time of 0.13 s.