BITS Faculty Publications
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Item Deep Learning based Time-Frequency Image Enhancement Method for Machinery Health Monitoring(IEEE, 2023) Choudhury, Madhurjya DevReliable machinery health monitoring using measured vibration signals requires a good readability of time-frequency (TF) images. However, conventional TF methods suffer from a limited time–frequency resolution and cross-term interferences, which limit their practical applicability in health monitoring. To address this issue, a TF image improvement method using deep learning is proposed in this paper. The proposed method employs a deep learning technique known as conditional generative adversarial network (cGAN) to convert a noisy low-resolution TF image of a bearing vibration signal into a noise-free high-resolution image such that the true frequency characteristics of measured signals may be revealed. In this paper, the cGAN model is trained using a simulation-based dataset generated from a bearing analytical model. The trained cGAN model is then utilized to improve TF images generated from real bearings under different fault and operating conditions. The results reveal that the proposed image improvement method generates high-resolution TF representations which are better than both the traditional TF images and those generated using TF reassignment methodsItem A Deep Learning Based Fault Diagnosis Method Combining Domain Knowledge and Transfer Learning(IEEE, 2023-11) Choudhury, Madhurjya DevDeep learning (DL) based fault diagnosis methods have gained considerable attention in the field of machine health monitoring due to their powerful feature learning capabilities. However, embedding domain diagnosis knowledge into the DL framework to obtain enhanced features having better correlation with the exact health conditions of machine elements for improved fault predictions is still an open challenge. In this paper, a fault diagnosis method combining two-dimensional (2D) image representations of squared envelop spectrum (SES) of vibration signals of bearings, a critical machine element, and a pretrained convolutional neural network (CNN) is proposed. SES is one of the most efficient indicator for the assessment of second order cyclostationary symptoms of damages, which are typically observed in bearings. In this paper, we integrate this knowledge in designing a DL framework for efficient fault diagnosis in bearings. The proposed method is tested and evaluated on an experimental bearing vibration dataset collected under different operating and fault conditions. Experimental results demonstrate that the proposed method can achieve a high diagnosis accuracy and present a better generalization ability both in balanced and imbalanced data scenarios