https://dspace.iiti.ac.in:8080/jspui/handle/123456789/76 2026-05-18T12:16:33Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18405 Title: Time Varying Mesh Stiffness Estimation of Cracked Polymer Gear Pair Considering Modified Contact Stiffness with Experimental Validation Authors: Yadav, Santosh; Parey, Anand Abstract: Purpose: Polymer gears are increasingly used as an alternative to metal gears in power transmission due to their advantages such as lightweight, low noise and vibration. However, crack induced failures remain a critical concern. Vibration signal analysis, governed by time varying mesh stiffness (TVMS), is an effective tool for fault detection. Hertzian contact stiffness (HCS) plays a critical role in TVMS estimation, and several models exist to estimate it. This study proposes a modified Hertzian contact stiffness (MHCS) model and a TVMS estimation model that accounts for pitch point cracks. Methods: The proposed MHCS formulation is incorporated into a TVMS model to estimate the mesh stiffness of cracked polymer gear. Finite Element Method (FEM)simulations are performed to validate the developed model. Further, dynamic modelling of a polymer gear pair has been done by employing proposed TVMS for a cracked gear and the vibration response is experimentally validated. Results: The proposed MHCS model shows improved accuracy in predicting contact stiffness. The results demonstrate that pitch point cracks cause significant fluctuations in TVMS, which are effectively captured by the developed model. The dynamic response analysis reveals distinct vibration signatures corresponding to crack presence, and experimental validation confirms good agreement with the predicted results. Conclusion: The proposed model effectively predicts the mesh stiffness and vibration characteristics of a cracked gear system, providing valuable insights through vibration analysis for detecting tooth cracks. © Springer Nature Singapore Pte Ltd. 2026. 2026-01-01T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18397 Title: Effect of Sensor-and Field-Based Parameters on Deforestation Mapping with Time-Series C-Band and L-Band SAR Data Authors: Sabir, Anam; Khati, Unmesh Abstract: Deforestation mapping is an important application for keeping an account of carbon loss and sequestration. To continuously monitor forested areas, it is important to have a consistent time-series data set that is fulfilled by Synthetic Aperture Radar (SAR). Based on the temporal signature of the SAR backscatter, forests can be monitored, and abrupt changes in the backscatter can be studied and investigated. The wavelength of the SAR signals largely governs the interactions with the targets. In this study, we analyze the performance of the open-source C-band and L-band SAR datasets from Sentinel-1 (S-1) and ALOS-2/PALSAR-2 ScanSAR. We use a statistical change detection algorithm, Cumulative Sums of Change (CuSUM) to identify the point of change in the temporal datasets. An increase in L-band copol backscatter was observed after deforestation in peat swamp forests, attributed to enhanced double bounce scattering. In contrast, deforestation of mangroves resulted in a decrease in backscatter. Using the C-band SAR data, the VH polarization provides accurate change maps with an average overall accuracy of 0.76 (Kalimantan) and 0.8 (Mondah). The results show that the Dual-Pol Radar Vegetation Index (DPRVI) provides accurate and homogeneous change maps with L-band SAR data with an average overall accuracy of 0.58 (Kalimantan) and 0.62 (Mondah). © 2025 IEICE Electronics Society. 2025-01-01T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18390 Title: Framework for Classifying Fault Types in Power Networks Using Machine Learning Authors: Maity, Sourav; Paladhi, Subhadeep Abstract: The reliable operation of power systems hinges on the swift and accurate classification of fault types. Conventional fault type classification methods typically employ intricate rule-based or model-based systems, which may struggle to adapt to the dynamic and diverse nature of fault scenarios. In recent years, machine learning (ML) algorithms have emerged as promising tools for enhancing fault type classification processes in power systems. These algorithms excel in learning patterns from data without the need for explicit programming. This study provides a comprehensive review and analysis of various ML techniques applied to fault type classification in power systems. It addresses key challenges such as the variability of fault types, the non-stationary behavior of signals, and the requirement for real-time response. The paper explores a range of machine learning algorithms including support vector machines (SVMs), neural networks, decision trees, and ensemble methods, highlighting their respective strengths and weaknesses in fault type classification scenarios. Moreover, the study offers insights into critical aspects like feature selection and pre-processing techniques that play pivotal roles in optimizing the performance of ML models in fault detection tasks. Evaluation metrics such as accuracy, sensitivity, specificity, and computational efficiency are used to assess the efficacy of each approach. © 2025 IEEE. 2025-01-01T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18399 Title: Analytical and Numerical Study of Fractional Logistic Equation With Variable Kernel in the Caputo Sense Authors: Singh, Sanjeev Abstract: We consider a fractional logistic equation involving a Caputo-type fractional derivative of order (Formula presented.) with a variable kernel (Formula presented.), a formulation introduced for its versatility in modeling complex real-world phenomena through an appropriate selection of fractional derivatives. The equilibrium points are identified, and their stability is rigorously analyzed using the (Formula presented.) Laplace transform technique. The existence and uniqueness of the solution are established via the fixed-point theorem. Furthermore, we express the analytic solution as an infinite series by introducing the fractional (Formula presented.) series expansion, which has a positive radius of convergence. By truncating this series, we demonstrate its practical applicability for various kernel functions and different values of (Formula presented.). Additionally, we present an innovative adaptive predictor–corrector method for solving initial value problems (IVPs) that involve a Caputo-type fractional derivative with a variable kernel, taking graded meshes into account. We conducted extensive numerical simulations across various fractional orders and kernels, demonstrating that the obtained results closely align with exact solutions in the integer case, as well as with the truncated (Formula presented.) -series expansion when a large number of nodes are used. Moreover, our approach exhibits satisfactory numerical stability in fractional scenarios. © 2026 John Wiley & Sons Ltd. 2026-01-01T00:00:00Z