BITS Faculty Publications
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Item A Critical Investigation of Hilbert-Huang Transform Based Envelope Analysis for Fault Diagnosis of Gears(IEEE, 2018) Choudhury, Madhurjya DevHilbert-Huang Transform (HHT) has been extensively used for fault diagnosis due to its capability in handling amplitude-frequency modulated (AM-FM) and multicomponent signals. However, its performance in handling the signals having complexity such as arising from practical gearbox measurements, like speed and load variation is debatable. This study is conducted to understand the use of traditional HHT in gear fault diagnosis by carrying out a literature survey followed by an envelope spectrum (ES) analysis for gear fault detection. The investigation demonstrates the capability of HHT in decomposing a multicomponent fault signal into its different meaningful modes, which can then be exploited for diagnosis. HHT demodulated envelope signal spectrum is found to be effective in revealing fault induced peaks during constant speed operation but its performance deteriorates under speed variation. Various gear fault simulation models are investigated to validate the effectiveness of HHT and a discussion is provided to conclude the paper.Item 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 scenariosItem An Overview of Fault Diagnosis of Industrial Machines Operating Under Variable Speeds(Springer, 2021-04) Choudhury, Madhurjya DevThis paper provides an overview of the recent advances made in the field of fault diagnosis of industrial machines operating under variable speed conditions. First, the shortcomings of the traditional techniques in extracting reliable fault information are laid down, followed by a discussion on the different approaches adopted to overcome these issues. Next, these approaches are discussed by categorizing them as resampling based and resampling free methods. The principle and implementation procedures of these methods are discussed by summarizing the key literature in this area. Finally, the paper is concluded by highlighting the future challenges to address in this area.