BITS Faculty Publications
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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-rigItem Deep Learning based Time-Frequency Image Enhancement Method for Machinery Health Monitoring(IEEE, 2023) Choudhury, Madhurjya DevReliable machinery health monitoring using measured vibration signals requires a good readability of time-frequency (TF) images. However, conventional TF methods suffer from a limited time–frequency resolution and cross-term interferences, which limit their practical applicability in health monitoring. To address this issue, a TF image improvement method using deep learning is proposed in this paper. The proposed method employs a deep learning technique known as conditional generative adversarial network (cGAN) to convert a noisy low-resolution TF image of a bearing vibration signal into a noise-free high-resolution image such that the true frequency characteristics of measured signals may be revealed. In this paper, the cGAN model is trained using a simulation-based dataset generated from a bearing analytical model. The trained cGAN model is then utilized to improve TF images generated from real bearings under different fault and operating conditions. The results reveal that the proposed image improvement method generates high-resolution TF representations which are better than both the traditional TF images and those generated using TF reassignment methodsItem A Digital Twin Based Framework for Real-Time Machine Condition Monitoring(IEEE, 2023) Choudhury, Madhurjya DevCondition Monitoring (CM) is an important approach to extending the life of complex equipment by forecasting the outcome of an event before catastrophic failure occurs. Recent advancements in digital twins (DT) offer additional benefits to machine condition monitoring. In this study, a framework based on DT for real-time condition monitoring of industrial machines is proposed. The multi-layer DT framework consists of a physical entity (PE), virtual equipment (VE), edge device, fidelity service and digital twin services. The virtual equipment is a replica of the physical entity or the monitored machine. It also contains a cloud platform to store data online and an application to interface with the cloud enabling users to check the data remotely. The fidelity service ensures conformity between the PE and the VE. The digital service provides optimal operation and maintenance schedules based on the data from both physical and virtual spaces. The integration of the edge layer enables real-time handling of high-frequency machine data for effective health monitoring. The validity of the proposed framework is demonstrated with a case study based on monitoring a critical component of an industrial drivetrain test rig. The features of the framework allow end-users to visualize the component's real-time health status remotely.Item A Deep Learning Based Fault Diagnosis Method Combining Domain Knowledge and Transfer Learning(IEEE, 2023-11) Choudhury, Madhurjya DevDeep 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