Department of Chemical Engineering

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Author: search.filters.author.Pani, Ajaya KumarSubject: search.filters.subject.Fault detection

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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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    Optimizing process monitoring efficiency through control limit adjustment in multivariate ewma charts
    (IEEE, 2024-10) Pani, Ajaya Kumar
    Statistical process control charts have proven to be helpful in process monitoring. The majority of previous research on SPC charts has been on univariate scenarios. This study builds a multivariate exponentially moving average (MEWMA) chart to perform process monitoring in a continuous stirred tank reactor (CSTR). The normal and faulty data were obtained from the Simulink model of CSTR. Smoothing parameter of EWMA was optimized to maximize process monitoring efficiency. False alarm rate (FAR) and fault detection rate (FDR) were used for calculating the monitoring efficiency. A novel control limit calculation combining T2 and square prediction error (SPE) is proposed to increase the accuracy of MEWMA technique.
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    Independent component analysis application for fault detection in process industries: Literature review and an application case study for fault detection in multiphase flow systems
    (Elsevier, 2023-03) Pani, Ajaya Kumar
    In process industries, early detection and diagnosis of faults is crucial for timely identification of process upsets, equipment and/or sensor malfunctions. Machine learning techniques using process data can be used as efficient process monitoring tools and is an active research area in the past two decades. The technique of independent component analysis (ICA) is a viable alternative to the widely used principal component analysis method. In this article, the basic ICA technique, its advantages, limitations and the various improvements proposed over the years are reviewed. Further, a detailed survey of ICA based techniques for process monitoring is presented. Finally, the application of ICA along with selection of independent components by negentropy calculation and control limit and monitoring index calculation is illustrated by an industrial case study of multiphase flow system