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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
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    Order Tracking Using Variational Mode Decomposition to Detect Gear Faults Under Speed Fluctuations, in 11th Annual Conference of the PHM Society 2019
    (Prognostics and Health Management Society, 2019-09) Choudhury, Madhurjya Dev
    Fault detection in gearboxes plays a significant role in ensuring their reliability. Vibration signals collected during gearbox operation contain a wealth of valuable condition information that can be exploited for fault detection. However, in an industrial environment machine operating speed always fluctuates around its nominal value, which causes smearing of the gearbox vibration spectrum. Considering operating speed fluctuation and multi-component nature of measured gearbox vibration signals, an order-tracking method combining the variational mode decomposition (VMD) and the fast dynamic time warping (FDTW) is proposed in this paper. Firstly, the multi-component vibration signal is decomposed into several intrinsic mode functions (IMFs) using VMD in order to extract a signal component with higher signal-to-noise ratio (SNR). Then, the sensitive fault information carrying IMF is exploited to estimate the instantaneous speed profile in order to construct the shaft rotational vibration signal. The measured vibration signal is then resampled based on the optimal warping path obtained by FDTW, which performs an “elastic” stretching and compression along the time axis of the extracted shaft vibration signal with respect to a sinusoidal reference signal of constant shaft rotational frequency. Finally, the gear fault is detected by constructing the order spectrum of the resampled vibration signal. The effectiveness of the proposed algorithm is demonstrated using simulation results.
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    A Methodology to Handle Spectral Smearing in Gearboxes Using Adaptive Mode Decomposition and Dynamic Time Warping
    (IEEE, 2021-02) Choudhury, Madhurjya Dev
    Tacho-less order tracking is an effective tool for fault detection in gearboxes operating under speed fluctuations. This technique's performance depends on extracting instantaneous shaft speed information from a complex multicomponent gearbox signal, which is first preprocessed by a bandpass filter. However, spectral overlap resulting from the time-varying frequency components makes it challenging to isolate the shaft speed harmonic mono-component using this approach. In order to overcome such issues, an adaptive tacho-less order-tracking (OT) method combining the variational mode decomposition (VMD) and the fast dynamic time warping (FDTW) is proposed in this article. The proposed method first decomposes the measured gearbox vibration signal using VMD to estimate the instantaneous shaft speed profile to construct the shaft vibration signal. The gearbox vibration signal is then resampled based on the optimal warping path obtained by FDTW, which performs an “elastic” stretching and compression along the time axis of the extracted shaft vibration signal with respect to a sinusoidal reference signal with a constant shaft rotational frequency. Finally, the presence of gear fault is detected by constructing the order spectrum of the resampled vibration signal. The effectiveness of the proposed algorithm is demonstrated using both simulation analysis and experimental validation using measurements from two different wind-turbine gearboxes collected under test and field conditions, respectively.
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    A novel tacholess order analysis method for bearings operating under time-varying speed conditions
    (Elsevier, 2021-12) Choudhury, Madhurjya Dev
    Order tracking (OT) of vibration envelope signal based on instantaneous frequency (IF) estimated from a time-frequency representation (TFR) is generally applied to detect bearing defects under time-varying speed conditions. However, these TFR based OT approaches require a high resolution for accurate estimation of the IF. To overcome these issues, a tacholess OT technique based on the fast dynamic time warping (FDTW) algorithm is applied to detect bearing defects for the first time in this paper. FDTW based signal transformation is initially applied to align a filtered shaft signal to a reference signal of constant frequency, followed by reconstructing the bearing envelope signal. The resulting envelope signal is able to provide a richer fault indication than the original signal. The efficacy of the developed OT method is established using a simulation analysis followed by experimental validation using measurements obtained from a laboratory test-rig and a wind turbine bearing.