Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs XGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, XGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

Seismic Fault Detection is a crucial aspect of oil exploration. While traditional deep learning methods struggle to handle complex seismic data patterns, training a deep learning model solely on synthetic seismic data may not yield satisfactory results. This research paper, involves utilizing a pre-trained vision-based transformer to extract relevant features from seismic data. By leveraging the knowledge learned from a different but related task, the model can capture general fault patterns in field data. In this framework, a portion of the pre-trained model architecture is employed and trained using a Structural Similarity-based loss function to learn fault-related features. This allows the model to adapt to the faulted structures of a geological dataset and improve fault detection performance in field data applications. In comparison to the state-of-the-art method, the proposed method yields improved results on real field dataset.

Early fault detection in induction motors is critical for industrial reliability, with rotor faults representing challenging diagnostic scenarios due to their gradual development and subtle manifestations. Conventional fault diagnosis techniques suffer from limited effectiveness in early-stage detection and poor performance under variable operating conditions. This paper presents a novel Digital Twin framework enabling real-time motor behavior simulation, generating graph representations that capture spatial-temporal relationships between rotor components. Graph Convolutional Networks (GCNs) learn from these graph-encoded representations, leveraging neighborhood connectivity for enhanced fault feature extraction. The methodology encompasses signal acquisition from a 32-bit processor-based digital twin simulator, coupled circuit modeling of rotor fault conditions, graph construction encoding physical relationships, and specialized GCN architecture with anomaly detection capabilities. Comprehensive validation using simulated and experimental datasets demonstrates robustness under variable load conditions. The proposed method achieves 93% accuracy using GCN combined with CatBoost, significantly outperforming conventional approaches. This integration enhances real-time fault diagnosis capabilities, enabling proactive predictive maintenance and improving operational reliability.

Subsurface lithology identification from well log signals is a crucial step in geological exploration, providing essential information about rock formation properties and fluid flow. Accurate identification of lithofacies aids in reservoir characterization and hydrocarbon exploration. This letter presents a novel approach for lithofacies identification from well logs using graph-based feature extraction and classification. The existing instance-based methods ignore the sequential information in well log signals, which can provide valuable insights about the local lithology. The proposed approach treats each instance in a temporal sequence as a node in a graph that captures the local geological information by aggregating temporally neighborhood nodes to create an embedded feature space. Two separate aggregating schemes are proposed, one using a spatial kernel approach and the other using an attention-based network layer, to find the nonlinear relationship between the feature vectors and give more weights to the nearest vectors in the feature space. The graph structure allows the network to incorporate spatial and relational information between different well log features into the classification process, leading to improved accuracy of predictions. The experiment is run on real-world data from the oil and gas exploration field at Krishna-Godavari (KG) Basin, India. The proposed method outperforms traditional feature-based classification and provides a unique way to enhance the representation of the well log signals for lithofacies classification tasks.

Geological fault detection is a crucial aspect of oil exploration. With the advancements in deep learning, the challenging task of accurate fault detection has gained popularity. While traditional deep learning methods struggle due to the small sample problem and the labor-intensive fault labeling process, training a deep learning model solely on synthetic seismic data may not yield satisfactory results due to the disparities between synthetic and real seismic data. To mitigate the impact of these differences, we propose employing an instance weighting (IW)-based transfer learning (TL). This approach involves utilizing a pretrained deep-learning model to extract fault-related features from seismic data. By leveraging the knowledge learned from a different but related task, the TL model can capture general fault patterns that can be applicable to real seismic data. In this framework, a portion of the pretrained model is employed to learn fault-related features, which can then be fine-tuned using a smaller amount of labeled real seismic data. This allows the model to adapt to the complexities of the actual geological situation and improve fault detection performance in field data applications. The proposed method has been tested on the Indian Krishna Godavari Basin dataset. The method yields satisfying results in spite of the high imbalance between the fault and nonfault classes.

This article presents a data-driven probabilistic framework for estimating missing values in multivariate and time-varying interdependent IoT data streams during reefer container monitoring. It models the periodic fluctuations in the temperature and humidity due to the refrigeration cycle using a log-normal distribution, followed by the estimation of missing values using the sparse vector autoregression (sVAR) model. The accuracy of sVAR is improved by considering the spatio-temporal correlation of sensor signals while computing the model parameters and a kernel-based weighting scheme. It is applied to a dataset collected during an experiment. The results show that it outperforms a few baseline methods while providing a comprehensive solution for both point-missing values as well as large gap situations considering co-occurring and non-co-occurring cases.

In recent years, there has been significant progress in utilizing neural networks and deep learning methods for enhancing the delineation of seismic faults. However, the scarcity of labeled data has posed a challenge in training such networks, leading to a reliance on synthetic samples. Consequently, the task of annotation has become a crucial component within machine learning frameworks. Data labeling not only consumes considerable time but also necessitates a high level of precision. To address the above limitations, an Artificial Intelligence-powered interactive annotation tool has been developed. This tool aims to minimize the immense human effort involved in labeling data by offering an efficient and accurate solution. By leveraging the power of artificial intelligence, the tool enables faster and more precise annotation. The effectiveness and reliability of the proposed tool are affirmed through the observed enhancements in segmentation quality and the average speedup achieved in the annotation process.

Seismic sparse layer inversion (SLI) plays a crucial role in improving the resolution of seismic data for accurate subsurface characterization. However, achieving high accuracy and efficient runtime in SLI is of paramount importance for reservoir characterization. In this paper, we address the SLI problem by constructing a dictionary using odd and even reflection coefficients. The conventional Linear Programming (LP) approach suffers from the equal penalization of all model parameters, resulting in ghost layers and high computational costs on Central Processing Units(CPUs). To overcome these limitations, we propose an enhanced formulation by incorporating Tikhonov regularization, dynamically penalizing the model parameters based on the target seismic trace. Moreover, we optimize the computational runtime by leveraging the power of a Graphical Processing Unit (GPU)-based LP solver. Our algorithm is implemented on an NVIDIA RTX 4000 GPU with 8GB dedicated memory and tested on the Indian WADU dataset. Results demonstrate the efficacy of our proposed method in accurately delineating thin seismic layers compared to state-of-the-art techniques. Additionally, we highlight the superior runtime performance of the GPU-based LP formulation compared to its CPU implementation, further enhancing the efficiency of the SLI process.

Seismic fault detection has become an important research topic in geo-science. Semblance-based coherence algorithm is widely used to detect seismic faults, folds, and fractures. However, the algorithm is computationally expensive on Central Processing Unit (CPU) when the seismic datasets are too large. Also, existing commercial geoscience software solutions use serial or batch processing modes using CPU-based computation which leads to a long execution time. In this paper, we present a fast and parallel implementation of semblance algorithm using General Purpose Graphical Processing Unit (GPGPU) powered with Compute Unified Device Architecture (CUDA). This is accomplished with a parallel kernel map of the algorithm through multiple threads. We also adopted a strategy for efficient memory occupancy and CPU-GPU communication with minimal latency. The algorithm is implemented on NVIDIA RTX 4000 GPU model with 8GB dedicated GPU memory and tested with Netherland F3 and Indian Krishna-Godavari (KG) Basin datasets. Our CUDA implementation achieved considerable speedup over its conventional CPU implementation on both datasets. Also, the proposed algorithm achieves faster times speedup are reported on both datasets over the commercial software OpenDtect. For extensive study, optimal runtime of the algorithm with variation of the parallel threads is also reported here.

Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.

The paper aims at proposing a non-invasive method for monitoring of the quality determining factors of Indian mangoes. The framework shows an IoT architecture in global mango cold chain using edge computing. Positioned at the inside of the reefer container, the system performs the task of sampling physio-chemical data from its integrated sensors and records the dynamic changes in continuous data streams. These records are treated in batches and incase of sensor events, the device triggers an alarm to a remote administration system hosted in Google cloud. GSM (during land transport) and on board Wi-Fi (during sea transport) are used as communication protocols during the phases of the journey. The data generated from the devices have been calibrated with reference to a standard Industry grade meter and compared with other similar systems.

Temperature is an important factor that controls the quality and shelf life of perishable produce after harvest. Hence during transcontinental export refrigerated container is used so as to minimize the degradation of food quality. This study aims to gain more insight into the cooling heterogeneity and quality decay of single Indian mangoes during export with the help of numerical modelling by computational fluid dynamics. For this purpose, digital twins of three most exported mango varieties namely Alphonso, Totapuri and Kesar, are created which accurately simulates the cooling behavior of real mango based on airflow rate and its temperature. Three airflow rates are modelled: 0.5 m/s, 2.5 m/s and 5.5 m/s. Kesar mango was found to have highest cooling rate and Totapuri mango the lowest. An increase in airflow rate was found to increase the cooling rate but up to a certain extent. A velocity of 2.5 m/s is considered optimum for cooling of mangoes and further increase does not have much effect on cooling rate. The overall quality variation is not much significant for different varieties of mango. The quality decay after one day was found to be 3% and 13% for different quality attributes.

Matrix Factorization (MF) techniques have already shown its strong foundation in collaborative filtering (CF), particularly for rating prediction problem. In the basic MF model, the use of additional information such as social network, item tags along with rating has become popular and effective, which results in making the model more complex. However, there are very few studies in recent years, which only use the users rating information for the recommendation. In this paper, we present a new finding on exploiting Projected User and Item Graph in the setting of Kernelized Probabilistic Matrix Factorization (KPMF), which uses different graph kernels from the projected graphs. KPMF works with its latent vector spanning over all users (and items) with Gaussian process priors and tries to capture the covariance structure across users and items from their respective projected graphs. We also explore the ways of building these projected graphs to maximize the prediction accuracy. We implement the model in five real-world datasets and achieve significant performance improvement in terms of RMSE with state-of-the-art MF techniques.

The popularity of online auctions and the associated frauds have led to many auction sites preferring English auction over other auction mechanisms. The ease of adopting multiple fake identities over the Internet nourishes shill bidding by fraudulent sellers in English auction. In this paper, we derive an equilibrium bidding strategy to counteract shill bidding in an online English auction. An algorithm based on this strategy is developed. We conduct experiments to evaluate the strategy in a simulated eBay like auction environment. Five popular bidding strategies are compared with the proposed one. In the simulation, bidders compete to buy a product in the presence of a shill. Each bidder is randomly assigned a bidding strategy. She draws her valuation from a uniform distribution. The experiments show hat the average expected utility of agents with proposed strategy is the highest when the auction continues for a longer duration.

Preserving privacy during Web transactions is a major concern for individuals and organizations. One of the solutions proposed in the literature is to maintain anonymity through group cooperation during Web transactions. The lack of understanding of incentives for encouraging group cooperation is a major drawback in such systems. We propose an anonymizing club mechanism, and sequential economic strategy for trusted collaboration. We model the individual transactions as a Prisoners' Dilemma, where two players either cooperate or defect while maintaining each other's anonymity. The activities of the participants over a series of transactions can be modeled as a sequential repeated game. We determine conditions to ensure cooperation among the participants in the sequential repeated game, even if defecting is a dominant strategy in each individual Prisoners' Dilemma game. Our results show that by adopting an appropriate initiation fee and adequate fine for malicious behavior, both enforced through a trusted central authority, we can sustain cooperation in the proposed anonymizing club mechanism.