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.Item Bridge Configured Wounded Switched Reluctance Motor(Elsevier, 2016) Choudhury, Madhurjya DevConventional switched reluctance motors suffer from high undesired acoustic noises and vibrations caused by the production of a considerable amount of radial force due to non-uniformity in air-gap which can be controlled in order to make it more efficient. A feasible solution to pacify this problem is the introduction of a special winding scheme called bridge configured winding (BCW) in switched reluctance motor (SRM). Various winding configurations (generally dual set of windings) have been developed till date in order to produce radial force in SRM. This paper presents the incorporation of bridge configured winding capable of producing both the torque and a controllable radial force using a single set of winding, thus reducing the use of additional winding for radial force production.Item Design and Analysis of a Radial Active Magnetic Bearing for Vibration Control(Elsevier, 2016-05) Choudhury, Madhurjya DevVibration caused by rotor unbalance is one of the most pertinent problems facing the rotating machines, including electrical motors and turbo machinery among others. Thus vibration attenuation has become very essential in improving the overall performance of such machines. In this paper, a 12-pole radial Active Magnetic Bearing (AMB), using AC excitation has been proposed to counteract the unbalance. Here a switching variation of AMB teeth excitation currents is implemented to generate a rotating force, synchronous with the rotor unbalance but in opposite direction.Item Modeling and analysis of bearingless switched reluctance motor equipped with specialized stator winding(IEEE, 2016) Choudhury, Madhurjya DevConventional switched reluctance motor undergoes extensive vibration and undesirable acoustic noise caused by the production of significant amount of radial force. The reason behind this is the non-uniformity in air-gap of the motor which can be controlled in order to make it more efficient. A viable solution to pacify this problem is the introduction of bearingless technology with a special winding scheme called bridge configured winding in switched reluctance motor. Various designs of winding have been introduced by researchers to produce radial force which can be utilized in order to make the motor bearingless. This paper presents the incorporation of a single set winding called bridge configured winding in a 12/8 switched reluctance motor capable of producing both torque and controllable radial force, which can be used for bearingless operation of the motor. An analytical model of a 12/8 switched reluctance motor equipped with bridge configured winding is developed considering the effects of magnetic saturation of the motor. A mathematical model is obtained by solving the magnetic equivalent circuit of the proposed design. Also a finite element model of the design is developed in ANSOFT Maxwell 2D in order to verify the developed analytical model.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 Vibration-based Condition Monitoring of Industrial Drivetrains Operating under Non-stationary Conditions(The Prognostics and Health Management Society, 2019-09-22) Choudhury, Madhurjya DevThis research is focused on advancing the state-of-the art in fault diagnostics of industrial drivetrains. It is proposed to develop a fault detection algorithm and fault severity prediction model for critical drivetrain components using measured vibration signals. The fault detection algorithm will be developed to overcome the challenge of extracting reliable fault information for drivetrains operating under speed and load fluctuations, whereas the severity prediction model will aid in predicting the degradation level of a faulty component. The results of this research can, therefore, help in improving the availability of industrial drivetrains, by providing a dependable platform to the maintenance personnel for proper decision-making.Item 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 DevFault 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.Item Internet-of-Things Enabled Smart Condition Monitoring System for Maintenance of Industrial Equipment, in International Symposium on Flexible Automation(JSTAGE, 2022-07) Choudhury, Madhurjya DevEquipment failures lead to unexpected downtimes that often result in a significant capital and productivity loss. A condition monitoring system (CMS) is critical in alleviating such equipment downtime and to ensure reliability by providing real-time analytics on condition monitoring (CM) data. However, many small and medium enterprises find CM data inaccessible due to the requirement of expensive hard-wired data acquisition system and the difficulty to install the additional hardware and sensors within the existing plant set-up. In this regard, Internet of Things (IoT) brings a new opportunity to CMS, increasing the system's ability to collect and analyze data with more flexibility. This paper presents a proof-of-concept prototype of an IoT-enabled CMS that utilizes edge-devices to remotely collect various measurements from industrial equipment and carry out real-time CM. Moreover, the system leverages a cloud, where history of machinery health state is stored. The cloud also enables real-time access of machinery health status either on demand or push notifications. This cloud service can be further expanded to facilitate the integration of advanced data analytic tools for predictive maintenance. The effectiveness of the implemented system is presented by demonstrating remote data acquisition and spectral analysis of CM signals from a rotating machinery test-rig