Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

The detection of anomalies and the prediction of corrosion defects in oil and gas pipelines constitute critical tasks for ensuring industrial safety and improving operational reliability. This study addresses the problem of regression-based prediction of corrosion defect levels (CR—corrosion defect) using operational process parameters. Machine learning methods, including Decision Tree, Random Forest, LightGBM, and CatBoost, were employed to develop predictive models. Data preprocessing was performed, including feature standardization and hyperparameter tuning using KFold cross-validation. Model performance was primarily evaluated using the Root Mean Square Error (RMSE) on both training and test datasets, as this metric is more sensitive to large prediction errors, which is particularly important in the context of corrosion defect analysis. Additionally, Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) were used to provide a comprehensive assessment of model accuracy and robustness. Experimental results demonstrate that the CatBoost model achieved the best performance, yielding the lowest RMSE on the test dataset (0.02040) with a close value on the training dataset (0.01682), indicating strong generalization capability. Furthermore, this model outperformed the others in terms of MSE, MAE, and R² on the test dataset (MSE = 0.000418, MAE = 0.006319, R² = 0.695544). The obtained results confirm the effectiveness of ensemble methods and gradient boosting algorithms for regression modeling of corrosion defect development processes in pipeline systems.

Расширенный (линеаризованный) фильтр Калмана (РФК) был развит для практически важных случаев, когда классические требования линейности модели объекта и измерений нарушаются, в следствие чего получаемые оценки могут расходиться. В статье приведены описана реализация РФК в задаче оценивания переменных состояния и параметров модели одномерного противоточного теплообменника, описываемого системой нелинейных дифференциальных уравнений. Приводятся результаты имитационного моделирования фильтра на основании экспериментального материала, полученного по модели в среде Unisim Design. Представлены статистические характеристики оценок, подтверждающие их сходимость, проанализированы корреляционные свойства их остаточных ошибок.

Pipeline systems for oil and gas transportation are complex distributed infrastructure facilities whose efficient and safe operation largely depends on the application of modern information and communication technologies. In the context of industrial digital transformation, intelligent monitoring systems capable of continuous acquisition, transmission, and analysis of telemetry data for the early detection of emergency conditions have become particularly relevant. One of the most critical challenges is the timely detection and accurate localization of leaks, which can result in significant economic losses, environmental damage, and threats to public safety. The objective of this study is to develop an approach for determining the coordinates of a pipeline leak based on intelligent processing of measurement data using machine learning methods. The proposed solution is intended for integration into information and communication systems for supervisory control and digital monitoring of pipeline transport. A two-layer multilayer perceptron implemented in the MATLAB environment is employed as the data analysis tool, enabling the development of a computationally efficient algorithm suitable for practical use in decision-support systems operating in near-real-time conditions. The neural network was trained on experimental datasets generated for various leak locations and flow rate values of the transported medium and was tested on independent datasets. The influence of the number of neurons in the hidden layer on leak localization accuracy was investigated. Maximum and root mean square localization errors were used as performance metrics. The results demonstrate that increasing model complexity by raising the number of neurons beyond 3–4 does not lead to a significant improvement in accuracy and may be accompanied by overfitting, thereby reducing the reliability of the algorithm when processing new data. It was found that the optimal neural network architecture comprises three neurons in the hidden layer, providing a root mean square error of approximately 2 km and a maximum error not exceeding 5.5 km. The obtained results confirm the effectiveness of neural network methods for intelligent analysis of telemetry information and demonstrate the feasibility of developing scalable information and communication systems for early leak detection. The practical significance of this work lies in improving the accuracy of accident localization and reducing pipeline operation risks through the implementation of intelligent data processing algorithms within digital industrial monitoring platforms.

В статье рассмотрена история появления и развития цифровых двойников, привлекающих при стальное внимание исследователей и пользователей. Было показано, что, в отличие от большого числа альтернативных определений, термин «цифровой двойник» содержательно раскрывает суть и разнообразие форм указанных инструментов, выявлена роль ЦД как неотъемлемого виртуального компонента киберфизической системы (КФС), отражающего в цифре свойства и поведение ее физического компонента (оригинала) на протяжении различных этапов его жизненного цикла. Рассмотрены базовые технологии построения и сопровождения цифровых двойников (ЦД) с учетом широкого разнообразия задач автоматизации технологических производств, решаемых с помощью ЦД. Сравниваются подходы к построению моделей технологических объектов – фундаментальное моделирование, моделирование на основе данных и гибридное моделирование.

Multi-parametric industrial time-series forecasting is important for predictive monitoring, soft sensing, and decision support in complex process industries. This study proposes a process-informed hybrid ARX-residual deep sequence architecture for short-horizon multi-output forecasting of fluid catalytic cracking unit (FCCU) product yields. The model combines a frozen linear ARX branch with causal temporal convolution, a three-layer GRU, causal self-attention, additive attention pooling, residual gating, and a consistency-regularized multi-output objective. Experiments were performed on an open simulated FCCU benchmark containing 20,160 one-minute observations from seven normal and disturbed operating scenarios. The leakage-controlled pipeline uses scenario-wise chronological splitting, train-only median imputation and standardization, exclusion of fault-timing metadata and direct algebraic target components, and 30 min causal windows for five-minute-ahead forecasting. Across ten prespecified random seeds, the proposed model achieved a historical-test RMSE of 0.022809 $\pm$ 0.000116, compared with 0.022751 for ARX-like Ridge. Dependence-aware statistical analyses did not establish a consistent statistical advantage for either model across the evaluated runs. Rolling-origin evaluation likewise showed similar point accuracy, while leave-one-scenario-out evaluation revealed severe extrapolation failure for the unseen pressure-drop scenario. The results support the reproducibility of the proposed architecture and the importance of the ARX-based dynamic structure, but they do not establish the statistical superiority of the full deep residual architecture. Independent industrial data or newly prespecified simulation trajectories are required for confirmatory external validation.