BITS Faculty Publications
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Item Bearing signal classification using dynamic time warping(Springer, 2025-03) Choudhury, Madhurjya DevBearing is a critical machine element whose fault-free health status is crucial to the reliable operation of the machine. In recent times, machine learning-based approaches of training models on huge amount of measured data are adopted to monitor the bearing health status. However, collecting a large number of complete fault samples in an industrial set-up is very expensive thereby fostering the need to introduce methods that have excellent classification and clustering performance without depending on an extensive amount of training data. To address this, a dynamic time warping (DTW)-based signal similarity measuring approach is proposed. In this paper, a reference signal corresponding to a particular bearing health class is generated, which is then warped by DTW on any test bearing signals to reveal the inherent similarity by calculating their cumulative distances. The distance measure is used to classify the bearing health. It is observed that DTW alone cannot obtain acceptable results when employed to handle complex bearing signals because of the presence of measurement noise and unwanted interfering components. An enhancement is proposed by integrating DTW-based similarity search with spectral kurtosis (SK)-based demodulation for enhanced classification. The proposed method is validated using the Case Western Reserve University (CWRU) bearing dataset.Item Domain generalization using pseudo triplet network learning for vibration signal-based fault diagnosis(IEEE, 2025-02) Choudhury, Madhurjya DevDomain generalization (DG) based intelligent fault diagnosis has developed rapidly in recent years owing to the need for applying trained neural networks to unseen domains. However, models trained using DG often suffer from performance degradation when in presence of nonstationary working conditions. To address this challenge, this work proposes a DG based intelligent fault diagnosis approach based on a vibration response mechanism guided pseudo triplet network, which extracts suitable features that correlate well with the health conditions. Firstly, the proposed approach estimates the cyclic spectral correlation maps of vibration signals to provide vibration response mechanism of different health conditions. Then, a pseudo triplet neural network is designed to calculate the distance between the representations of the prior input, the negative input from the representation of the main input. The prior input is the specific part of the cyclic spectral correlation map with the selected carrier band and it guides the network focus on the fault-related features. Finally, the proposed approach is evaluated through experiments conducted on data collected from nonstationary working conditions.Item An adaptive source-free unsupervised domain adaptation method for mechanical fault detection(Elsevier, 2025-04) Choudhury, Madhurjya DevCross-machine fault detection is crucial due to the challenges of data labeling. While domain adaptation methods facilitate diagnosis across rotating machines, they often require data sharing, which is impractical due to privacy concerns and large data transmission. Although domain generalization and source-free unsupervised domain adaptation (SFUDA) methods address privacy issues, most fail to consider dynamic distribution shifts within and between domains, limiting their effectiveness. To overcome this challenge, an adaptive SFUDA method named AI3M is proposed. The AI3M pre-trains a source model with intra- and inter-domain information maximization loss to reduce distribution shifts within and between domains, and then adapts the model with a target-guided adaptation strategy to minimize the dynamic gap between different machines. Experiments on datasets from 11 wind turbines across 8 wind farms show that the proposed method outperforms state-of-the-art DG and SFUDA approaches, achieving superior cross-machine fault detection performance.