BITS Faculty Publications

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Subject: search.filters.subject.Fault diagnosis

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    Industrial process monitoring using support vector data description: A systematic review and application for fault detection in multiphase flow system
    (Elsevier, 2025-12) Pani, Ajaya Kumar
    In the era of Industry 4.0, machine learning based data-driven techniques are increasingly explored for industrial process monitoring. This article presents a review on application of support vector data description (SVDD) for industrial process monitoring followed by the design of SVDD model for fault detection in a multiphase flow system. In the review section, the basic technique, open design issues and a detailed survey on industrial applications are presented. In the application part, PRONTO benchmark multiphase flow dataset, is used to design SVDD model for detection of three faults: air leakage, air blockage and diverted flow. The Gaussian kernel parameter of the SVDD model is determined using particle swarm optimization (PSO) and the starting value for PSO, is obtained from literature provided analytical formula. Simulation of PSO-SVDD models shows promising results for fault detection in multiphase flow system.
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    A Critical Investigation of Hilbert-Huang Transform Based Envelope Analysis for Fault Diagnosis of Gears
    (IEEE, 2018) Choudhury, Madhurjya Dev
    Hilbert-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.
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    A Deep Learning Based Fault Diagnosis Method Combining Domain Knowledge and Transfer Learning
    (IEEE, 2023-11) Choudhury, Madhurjya Dev
    Deep 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
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    An Overview of Fault Diagnosis of Industrial Machines Operating Under Variable Speeds
    (Springer, 2021-04) Choudhury, Madhurjya Dev
    This 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.
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    A Review on Fault Diagnosis of Misaligned Rotor Systems
    (International Journal of Performability Engineering, 2020) Jalan, Arun Kumar
    The diagnosis and prognosis of misaligned rotor systems have gained importance in recent times. Misalignment has become one of the main reasons for system vibration, which reduces the life and stability of machine parts, making it vitally important for machines to perform effectively without any catastrophic failure. Limited research has been reported on understanding its effect on rotor systems. Even if zero misalignment is achieved at the beginning, it cannot be retained over longer durations due to various reasons. Many techniques like DWT, CWT, and HHT are also used to understand the misalignment problem. Some advanced techniques such as MCSA, thermal imaging, and the acoustic emission technique have come into existence and become important tools to classify faults, leading to more reliable misalignment diagnosis. In the present study, a detailed literature review is conducted to diagnose and classify misalignments. All recent techniques and their limitations are discussed, and a hybrid approach is presented for the lucid understanding of this fault and its classification.