Modern Transportation Systems and Technologies

This article analyzes the prospects and possibilities of using permeable asphalt pavement in the climatic and economic conditions of the Perm Territory. The study is based on the works of Russian and international experts. The relevance of the topic is driven by the continuous development of road infrastructure and the increasing traffic intensity, including heavy-duty vehicles, which requires special attention, as well as the development of solutions to improve road safety.

The paper examines the properties of permeable asphalt concrete in comparison with stone mastic asphalt, their specific features and advantages, as well as general information about the Perm Territory as a region for implementing permeable asphalt pavement and the potential for better adapting the material to the regional conditions.

Aim. To develop a physics-informed machine learning model capable of diagnosing inter-turn short circuits in the windings of squirrel-cage induction motors with high accuracy.

Materials and Methods. This study utilized a neural network training method using stator current data and FFT analysis, along with programming in Python and C++. A laboratory test bench was created to simulate the signs of inter-turn short circuits in the motor stator winding.

Results. A machine learning model capable of detecting inter-turn short circuits in the stator windings of induction motors with high accuracy was developed. The volume of labeled data required for model training was determined. A laboratory test bench was created to simulate inter-turn short circuits in the motor winding.

Conclusion. The obtained results allow us to address a number of issues associated with the effective use of neural networks for diagnosing induction motors, including the need for large volumes of data, low interpretability, poor generalization ability, and high sensitivity to the quality of the recorded data.

Aim: To develop and present a methodology for processing mobile laser scanning (MLS) data that allows highly precise and reliable determination of the scope of work for justified and optimized road repair design, in contrast to existing approaches.

Materials and methods: MLS data were obtained in .las format; control points measured with a total station at 50-meter intervals; software: Microstation, Terrascan, Robur, and AutoCAD. The processing methodology included preliminary point cloud cropping and classification, scanner calibration against the asphalt concrete surface, data adjustment using control points with vector coordinate offsets, semi-automated vectorization of road markings based on reflectance intensity, and noise filtering through solid surface extraction.

Results: The method yielded a calibrated and filtered point cloud with a discrepancy in heights at control points of less than 2 cm. Road markings were identified based on reflectance intensity, and 3D polylines of the road edges and centerline were constructed. The solid surface operation eliminated noise, creating a smooth triangulated terrain model. ROBUR calculated rut depths, cross-sections, and a table of values. The final output determined the milling area, asphalt paving volume, and site repair plan.

Conclusion: The developed methodology for processing MLS data ensures vertical accuracy of less than 2 cm and reliable determination of milling and asphalt concrete paving volumes. It allows optimizing financial and labor costs of road repairs, unlike existing approaches. The methodology is recommended for implementation in road surveying for highway repairs.

Background: One of the key objectives of digital transformation in railway transport is the development of digital platforms and digital twins aimed at improving operational efficiency. Predicting the remaining useful life of equipment is a critical task for traction rolling stock and traction substations of traction power supply systems. A key component affecting the reliability of these assets is batteries. The development of a digital twin of a battery for traction rolling stock and substations enables the estimation of essential parameters, namely remaining capacity and remaining useful life (RUL).

Aim: To develop a digital twin of a battery for traction rolling stock and traction substations of traction power supply systems, providing estimates of remaining capacity and RUL based on a dual Kalman filter, using a mixed approach to state-of-charge (SoC) estimation and adaptive forgetting factors derived from measurements of current, voltage, and temperature.

Materials and methods: To achieve the stated aim, the research employed methods of experimental design, mathematical statistics, differential calculus, and a dual Kalman filter algorithm. Verification of the results was performed using experimental data from a lithium-ion battery with a series connection of 15 cells. The estimation of remaining capacity and RUL was based on joint filtering of the parameters of a Thevenin equivalent circuit model and the SoC.

Results: A digital twin of a battery for traction rolling stock and substations has been developed. The proposed digital twin enables the determination of remaining capacity and RUL from measured values of current, voltage, and temperature.

Conclusion: The results were verified against measurement data from standard control and monitoring systems. The mean absolute percentage error for the SoC and voltage estimation was less than 1.5% and 0.5%, respectively. The obtained estimates of the RUL and remaining capacity are reliable and consistent with the results of similar studies in charge–discharge cycles. The proposed method for estimating the remaining useful life and remaining capacity of batteries for traction rolling stock and substations is recommended for use in technical condition monitoring and diagnostic systems within the framework of the Digital Locomotive and Digital Substation projects.