Evaluation and Reduction of Energy Consumption of Railway Train Movement on a Straight Track Section with Reduced Freight Wagon Mass

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 280 - 280

Published: Jan. 10, 2025

This paper presents an evaluation and reduction of energy consumption during railway train movement on a straight track section with reduced freight wagon mass. A theoretical model was developed to simulate based input parameters, including speed, gradient, length, travel time, The results indicate that increases by 18.9% as speed rises 90 km/h gradients increase 2.0‰, while decreases 14.5% descending gradient 1.5‰, which corresponds the expected dynamics trains. These are supported experiments showing MAPE error does not exceed 1.9%, can confirm accuracy model. comprehensive analysis potential in mass also conducted. Using design 2.3% allows for 8–89 kW·h, depending length movement.

Language: Английский

---

Method of Helicopter Turboshaft Engines’ Protection During Surge in Starting Mode

Energies, Journal Year: 2025, Volume and Issue: 18(1), P. 168 - 168

Published: Jan. 3, 2025

This article proposes a mathematical model for protecting helicopter turboshaft engines from surges, starting with fuel metering supply and maintaining stable compressor operation. The includes several stages: first, is supplied according to specified program; second, an unstable operation signal determined based on the gas temperature in front of turbine generator rotor speed derivatives ratio; at third stage, when ratios’ threshold value exceeded, stopped, ignition system turned on. Then, restored reduced consumption, corrected, followed by return regular neural network implementing this method consists layers, including calculation, comparison threshold, correction consumption speed. input data are A instability generated if ratio exceed value, which leads adjustment regulation 28…32%. backpropagation algorithm hyperparameter optimization via Bayesian was used train network. computational experiments result TV3-117 engine semi-naturalistic simulation stand showed that proposed effectively prevents surge stabilizing pressure, vibration, reduces 29.7% under start-up conditions. Neural quality metrics such as accuracy (0.995), precision (0.989), recall (1.0), F1-score (0.995) indicate high efficiency method.

Language: Английский

---

Real-Time Analysis of Industrial Data Using the Unsupervised Hierarchical Density-Based Spatial Clustering of Applications with Noise Method in Monitoring the Welding Process in a Robotic Cell

Information, Journal Year: 2025, Volume and Issue: 16(2), P. 79 - 79

Published: Jan. 22, 2025

The application of modern machine learning methods in industrial settings is a relatively new challenge and remains the early stages development. Current computational power enables processing vast numbers production parameters real time. This article presents practical analysis welding process robotic cell using unsupervised HDBSCAN algorithm, highlighting its advantages over classical k-means algorithm. paper also addresses problem predicting monitoring undesirable situations proposes use real-time graphical representation noisy data as particularly effective solution for managing such issues.

Language: Английский

---

Helicopter Turboshaft Engines’ Neural Network System for Monitoring Sensor Failures

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 990 - 990

Published: Feb. 7, 2025

An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This enables sequential data processing while ensuring high accuracy anomaly detection. Using recurrent layers (LSTM/GRU) is critical dependencies among time series analysis identification, facilitating key information retention from previous states. Modules such as SensorFailClean SensorFailNorm implement adaptive discretization quantisation techniques, enhancing the input quality contributing to more accurate predictions. The demonstrated detection at 99.327% after 200 training epochs, with reduction loss 2.5 0.5%, indicating stability processing. A algorithm incorporating temporal regularization combined optimization method (SGD RMSProp) accelerated convergence, reducing 4 min 13 s achieving an of 0.993. Comparisons alternative methods indicate superior performance proposed approach across metrics, including 0.993 compared 0.981 0.982. Computational experiments confirmed presence highly correlated sensor method's effectiveness fault detection, highlighting system's capability minimize omissions.

Language: Английский

---

Entropy-extreme concept of data gaps filling in a small-sized collection

Egyptian Informatics Journal, Journal Year: 2025, Volume and Issue: 29, P. 100621 - 100621

Published: Feb. 10, 2025

Language: Английский

---

Freight wagon body design with increased load capacity

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 16, 2025

Increasing the load capacity of freight wagon bodies is a key issue aimed at improving energy efficiency and competitiveness rail transport. This study presents for first time design body with increased proposes new changes to floor. To verify proposed solution, CAD models floor thicknesses ranging from 3 6 mm were developed, calculations performed von Mises stresses, resultant displacements, equivalent strains, safety factors each model. The factor structure has been by 5.2 times. results indicated that modified 1.6% 2.7%, depending on thickness, compared baseline construction thickness 7 mm. In addition effectively increasing wagon's load, modifications maintain structural integrity address mass considerations. Furthermore, these allow use standard carbon steel, which provides additional economic benefits. confirms thinner materials in can significantly enhance overall performance operational

Language: Английский

---

An Intelligent Self-Validated Sensor System Using Neural Network Technologies and Fuzzy Logic Under Operating Implementation Conditions

Big Data and Cognitive Computing, Journal Year: 2024, Volume and Issue: 8(12), P. 189 - 189

Published: Dec. 13, 2024

This article presents an intelligent self-validated sensor system developed for dynamic objects and based on the concept, which ensures autonomous data collection real-time analysis while adapting to changing conditions compensating errors. The research’s scientific merit is that has been integrates adaptive correction algorithms, fuzzy logic, neural networks improve sensors’ accuracy reliability under operating conditions. proposed provides error compensation, long-term stability, effective fault diagnostics. Analytical equations are described, considering corrections related influencing factors, temporal drift, calibration characteristics, significantly enhancing measurement reliability. logic application allows refining scaling coefficient adjusts relationship between measured parameter utilizing inference algorithms. Additionally, monitoring diagnostics implementation states through LSTM enable detection. Computational experiments TV3-117 engine demonstrated high data-restoring during forced interruptions, reaching 99.5%. A comparative with alternative approaches confirmed advantages of using (Long Short-Term Memory) in improving quality.

Language: Английский

---