Intelligent Condition-Based Monitoring of Rotary Machines With Few Samples

Recently, intelligent condition based monitoring systems build on deep learning methods have gained popularity. The success of these methods relies upon the large labeled training datasets, which are crucial to collect in industries. Therefore, building an effective fault diagnosis system becomes challenging. In this paper, a novel fault diagnosis framework is proposed to tackle the issue of limited samples in the training dataset. In the proposed framework, firstly, new training samples termed as synthetic samples are generated to increase the size of the dataset. After that, both original and synthetic samples are stacked, and the classifier model is trained. This study proposes a modified Conditional Variational Autoencoder (CVAE) to generate synthetic samples. In the proposed CVAE, centroid loss is added to the standard CVAE objective function. This loss directs generated samples to remain close with the centroid of their respective class, which helps in generating synthetic samples quite similar to the original samples. This paper also investigates the performance of proposed model in the presence of noise and effect of transformed data and original data. To verify the effectiveness of the proposed approach, the Air compressor and Case Western Reserve University datasets have been investigated. For the CWRU dataset with only 80 samples, accuracy of 96.39%, 99.58%, 98.33% was obtained using multilayer neural network, support vector machine, and RF classifiers respectively. Classification accuracy increased to 63.33% when modified CVAE is used instead of standard CVAE. Finally, a comparative analysis between proposed methods and other state-of-the-art methods has been presented.