Machines

This article examines an integrated approach to data acquisition and transmission within an intelligent thermal conditioning system for engines and vehicles that operates using thermal energy storage and the digital twin concept. The system is characterized by its use of multiple primary energy sources to power internal subsystems and maintain optimal engine and vehicle temperature conditions. Building on a formalized conceptual model of the intelligent thermal conditioning system, the study identifies key technological features required for implementing complex operational processes, as well as the stages necessary for applying the proposed approach during the design and modernization phases throughout the system’s life cycle. A core block diagram of the system’s digital twin is presented, developed using mathematical models that describe support and monitoring processes under real operating conditions. Additionally, an architectural framework for organizing data collection and transmission is proposed, highlighting the integration of digital twin technologies into the thermal conditioning workflow. The article also introduces methods for adaptive data formation, transfer, and processing, supported by a specialized onboard software-diagnostic complex that enables structured information management. The practical implementation of the proposed solutions has the potential to enhance the energy efficiency of thermal conditioning processes and improve the reliability of vehicles employing thermal energy storage technologies.

Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life (RUL), are used as a crucial step in a framework of reliability-centered maintenance to increase efficiency. But modern methods of RUL forecasting fall short when dealing with real-world scenarios, where CPS are described by multidimensional continuous high-frequency data with working cycles with variable duration. To overcome this problem, we propose a new method for fault prediction, which is based on extraction of semantic state vectors (SSVs) from working cycles of equipment. To implement SSV extraction, a new method, based on convolutional autoencoder and extraction of hidden state, is proposed. In this method, working cycles are detected in input data stream, and then they are converted to images, on which an autoencoder is trained. The output of an intermediate layer of an autoencoder is extracted and processed into SSVs. SSVs are then combined into a time series on which RUL is forecasted. After optimization of hyperparameters, the proposed method shows the following results: RMSE = 1.799, MAE = 1.374. These values are significantly more accurate than those obtained using existing methods: RMSE = 14.02 and MAE = 10.71. Therefore, SSV extraction is a viable technique for forecasting RUL.

The use of physical vehicles and environments during vehicle research and development is highly resource-intensive, particularly for autonomous vehicles. Recently, digital models are therefore increasingly used instead, which require high levels of fidelity and validity. While the two aforementioned qualities are often lacking, an absence of versatility for multi-purpose use is even more prevalent in current digital models. In response to these challenges, this work presents a novel real-time multi-physics digital twin of an offroad vehicle with high levels of fidelity and validity, both regarding the vehicle dynamics and hydraulics, as well as regarding the visual representation of the environment and the exteroceptive sensor emulation. The versatility of the digital twin enables its usage for vehicle development tasks concerning mechanical components and driveline, as well as for visual machine learning tasks, such as generation of auto-annotated visual training data. Development of control algorithms leveraging both visual input and mechanical systems is also enabled. Furthermore, the real-time capability allows for Hardware-in-the-Loop and Vehicle-in-the-Loop simulation. The modeling, calibration, and real-world validation of the digital twin is presented, with an emphasis on the vehicle dynamics and hydraulics. The shown validity enables advancements in the development of autonomous offroad vehicles.