Condition Monitoring Research Articles

Multifunctional superhydrophobic composite film with icing monitoring and anti-icing/deicing performance

Icing can cause damage to outdoor equipment such as airplanes, wind turbine blades and power lines, which poses potential safety hazards. Recently, electrothermal superhydrophobic composite film with the synergistic effect of anti-icing and deicing properties can effectively hinder the formation and accumulation of ice. However, these superhydrophobic composite films have no icing condition monitoring property, which is critical to improve deicing efficiency and reduce energy consumption. In this paper, we have designed a multifunctional superhydrophobic composite film using the combination of laser ablation and spraying method, which exhibits excellent comprehensive anti-icing, deicing and icing monitoring properties. The experimental results demonstrated the icing delay time of the film reached 4.0 min at −20 °C. Meanwhile, taking advantage of the outstanding electrothermal effects of the laser induced graphene/carbon nanotubes (LIG/CNTs) composite conductive network, with DC voltage (5 V) excitation, the film temperature rapidly rose from −20 °C to 107 °C in 90 s, thereby effectively removing the ice. More importantly, due to the temperature sensing performance of the LIG/CNTs composite conductive network, it could monitor whole icing and deicing process of droplet with different volumes and temperatures in real time through the change of film resistance. Therefore, the comprehensive anti-icing, deicing and ice monitoring properties allowed it to effectively reduce icing hazards.

Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions.

Modeling of Ball Screw–Nut Interface Stiffness With Wear (Ball Screw Wear Dynamics)

Abstract The effects of wear, preload loss, and missing balls on the dynamics of ball screw drives in machine tools are modeled and incorporated into the finite element model of the drive assembly for condition monitoring. The contacts between the ball–nut and ball–screw are modeled using Hertzian springs, whose stiffnesses vary as a function of the worn contact area. These contact stiffnesses are then transformed to the finite element nodes on the nut and screw. The frequency response functions at the motor shaft and table, which can be measured by commercial computer numerical control (CNC), are predicted for various faults at different positions of the table. The experimentally validated model demonstrates that the faults primarily affect the first coupled torsional-axial mode of the ball screw drive and can be utilized for automated condition monitoring of ball screw drives.

Evaluation of Drivers’ Mental Model, Trust, and Reliance Toward Level 2 Automated Vehicles

Level 2 automated vehicles (AVs) have been commercially available for years, yet the extent to which drivers are cognizant of their capabilities and limitations remains largely unknown. This study aimed to assess the public’s mental model of Level 2 AVs and investigate the relationships between mental model, trust, and reliance. Moreover, it sought to explore driver heterogeneity through cluster analysis to identify typical driver classes. Employing Tesla Autopilot as a representative Level 2 automation system, we designed a questionnaire measuring mental model from three dimensions: functional condition, sensorimotor preparation, and automation monitoring. This questionnaire was administered to both Tesla Autopilot owners (N = 357) and non-owners (N = 357). The results showed that, on average, drivers possessed an inadequate mental model regarding Level 2 AVs, and those with a relatively better mental model exhibited lower levels of trust and reliance. Owning a Level 2 AV did not result in a better understanding of the system, but it led to a higher trust level. Moreover, the results suggested that drivers’ trust and reliance in AVs tended to increase within the initial 6 months of usage and then kept stabilized after that. Through cluster analysis, we identified three distinct driver classes that exhibited significant differences in their mental model levels and attitudes towards AVs: Class 1 (blurred positive), Class 2 (sober neutral), and Class 3 (cautiously positive). This further validates the presence of heterogeneity among drivers. The insights gained from this study can serve as a valuable resource for supporting and guiding the development and implementation of training programs aimed at enhancing drivers’ comprehension of Level 2 AVs.

A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults

Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

Reference Material for the Composition of Fuel Oil

Electrical transformers are a key element in the process of distributing electricity from the producer to the final consumer. The reliability of electrical transformers is ensured through systematic monitoring of the structural condition using chemical analysis of fuel (transformer) oil used for electrical insulation and cooling of transformers. The working life of energy oil and paper insulation can be assessed by evaluating the content of antioxidant additive (ionol) and furan derivatives (furfuryl alcohol, furfural, 2-acetylfuran and 5-methylfurfural). The article provides a critical analysis of methods for measuring ionol and furan derivatives in oils. In particular, it has been established that the most widely used methods for the identification of ionol and furan derivatives are liquid and gas chromatography methods with preliminary isolation of analytes based on the principles of liquid/solid-phase extraction. At the same time, the need for matrix reference materials with established metrological traceability was identified for metrological support of methods for measuring the content of ionol and furan derivatives in oils. Therefore, the purpose of this study was the development of a certified reference material (CRM) for the composition of fuel oil. The technology for producing such a CRM makes it possible to prepare the material with the required concentrations of analytes. The article describes the sequence of preparation of a CRM batch and presents the conclusions of studying the stability and between-bottle homogeneity of the material. The given calculation of the standard uncertainty for the certified value of the CRM from the characterization method, heterogeneity, and instability of the material in accordance with GOST ISO Guide 35–2015 deserves attention. The metrological traceability of certified values of the CRM to the unit of value «mass fraction of a component» reproduced by the State Primary Standard of units of mass (molar) fraction and mass (molar) concentration of organic components in liquid and solid substances and materials based on liquid and gas chromatography-mass spectrometry with isotope dilution and gravimetry (GET 208–2019). The competence of the study is confirmed by the fact that the CRM was recognized as GSO 12232–2023.

Experience of preputiotomy performing in bull with acroposthitis and fibrous affect of penis’ S-shaped curve

The aim of this study was to perform a preputiotomy with the formation of an artificial opening in a breeding bull to realize his reproductive potential. Scientific and production research was conducted in the spring of 2024. In a 3.5-year-old Holstein breeding bull, kept at a breeding station for keeping breeding bulls, acroposthitis and fibrous lesion of the penis in the area of the S-shaped bend were detected due to the developed infectious process caused by Pseudomonas aeruginosa. The resulting pathology prevented the penis from fully extending beyond the preputial sac when collecting sperm for an artificial vagina (the last sperm collection was performed on 02/07/2024). The operation performed consisted of the following stages: insertion of a thick-walled tube into the preputial sac for better orientation in the tissues; making a V-shaped incision of all layers of the prepuce near its fornix; excision of the prepuce section necessary for free removal of the penis when collecting sperm; formation of an artificial opening by connecting the parietal preputial layer to the skin with an interrupted suture. The postoperative period included daily monitoring of the animal's condition, antibiotic therapy (amoxicillin (0.15 g), three injections every 48 hours), and the use of non-steroidal anti-inflammatory drugs (meloxicam (0.002 g), 3 days). A month after the operation, sperm was collected (06.05.2024, three months later) and the presence of all unconditioned reflexes was noted. At the same time, the copulatory reflex was full, painless and ended in ejaculation. The obtained volume of sperm after two mountings was 2.5 ml, the concentration was 1.3x108, the activity was 8 points. Thus, preputiotomy is an effective method for surgical correction of lesions of the preputial sac and adhesions in the area of the S-shaped bend of the penis in breeding bulls.

Ultrasensitive Ultrasoft Buckled Crack‐Based Sensor for Respiration Measurement and Enhanced Human–Machine Interface

Wearable strain sensors have transformed the real‐time monitoring of health conditions and human–machine interactions. However, recently developed wearable strain sensors exhibit several limitations. For example, when a sensor is designed with high sensitivity to detect strain, it struggles to accurately measuring the deformation of low‐stiffness materials like skin. Additionally, finding the optimal balance between sensitivity, durability, hysteresis, and strain range in sensor design is challenging. To address these challenges, a Buckled, Ultrasoft, Crack‐based, Large strain, EpiDermal (BUCKLED) sensor is developed. This sensor integrates the benefits of soft structure engineering with high sensitivity of crack‐based sensing mechanisms to ensure optimal skin deformation measurements. The BUCKLED sensor exhibits significant improvements in compliance (18 500 mm N−1), stretchability (100%), hysteresis (2%), durability (10 000 cycles with 100% strain), and force sensitivity () owing to its buckled shape, confirming its ability to detect subtle movements with enhanced accuracy. The sensor's high compliance allows it to accurately measure low‐stiffness objects, ensuring reliable performance. Furthermore, the sensor's tunability is demonstrating its effectiveness in applications such as respiratory monitoring, facial expression recognition, and silent speech interfaces. Consequently, the proposed sensor is versatile and holds great potential for a wide range of sensing applications.

Enhancing Information Exchange in Ship Maintenance through Digital Twins and IoT: A Comprehensive Framework

This article proposes a comprehensive framework for enhancing information exchange in ship maintenance through the integration of Digital Twins (DTs) and the Internet of Things (IoT). The maritime industry faces significant challenges in maintaining ships due to issues like data silos, delayed information flow, and insufficient real-time updates. By leveraging advanced technologies such as DTs and IoT, this framework aims to optimize maintenance processes, improve decision-making, and increase the operational efficiency of maritime vessels. Digital Twins create virtual replicas of physical assets, allowing for continuous monitoring, simulation, and prediction of ship conditions. Meanwhile, IoT devices enable real-time data collection and transmission from various ship components, facilitating a seamless flow of information. This integrated approach enhances predictive maintenance capabilities, reduces downtime, and improves resource allocation. The article will delve into the architecture of the proposed framework, implementation steps, and potential challenges, supported by case studies that demonstrate its practical application and benefits. By addressing these aspects, the framework aims to provide a robust solution for modernizing ship maintenance operations and ensuring the longevity and reliability of maritime assets.

Developing a Calculation Workflow for Designing and Monitoring Urban Ecological Corridors: A Case Study

Urban ecological corridors play a crucial role in biodiversity conservation, connecting fragmented habitats in highly anthropized areas and generating benefits in terms of the sustainability of urban environments. These corridors mitigate the effects of habitat fragmentation, such as reduced genetic diversity and limited species dispersal, while improving the ecological health of urban environments and the well-being of citizens. This study proposes a calculation workflow for the identification of the necessary and most suitable ecological corridors to be planned in the urban-environmental planning phase and identifies some of the existing innovative technologies to evaluate and improve their functionality, enabling the real-time monitoring of habitat conditions and providing valuable information to optimize the design and management of these peri-urban natural areas. Urban ecological corridors also improve human well-being by contributing to cleaner air, better water quality and recreational opportunities to the point that the costs incurred for their construction are much lower than the economic and social benefits for the area.

Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach

Data-driven models have become powerful tools for structural and condition monitoring of engineering systems, particularly wind turbines. This paper presents a comparative analysis of common machine learning (ML) algorithms (artificial neural networks, linear regression, random forests, and gradient boosting) and a cointegration-based approach for fault detection using Supervisory Control and Data Acquisition (SCADA) data. While ML models offer early fault prediction, the cointegration method is simpler, requires less training data, and has lower computational costs. However, it is less effective for early detection. To balance these trade-offs, we propose a cascading monitoring framework, where the ML model provides long-term predictions (outer monitoring process) and the cointegration model offers short-term verification (inner monitoring process). The cointegration model serves to confirm anomalies flagged by the ML model. By combining both models in a cascade structure, the system reduces the risk of false alarms triggered by uncertainties in the ML model alone. Furthermore, the short-term cointegration-based prediction model helps pinpoint immediate risks and mitigate the issue of prolonged downtime. This combination enhances both accuracy and reliability, as demonstrated through testing on a five-year SCADA dataset from a commercial wind turbine with a known gearbox fault.

Application and Research of Intelligent Multi-source Information Fusion Technology in Firefighting Equipment

As modern firefighting environments become increasingly complex, traditional firefighter equipment and operational methods are no longer adequate to address the high-risk challenges of fire rescue operations. Fire scenes are characterized by complex and hazardous environmental factors, with firefighters facing multiple threats such as extreme temperatures, dense smoke, and toxic gases. Therefore, ensuring the safety and efficiency of firefighters is of paramount importance. This study integrates multiple sensor technologies to enable real-time monitoring of firefighters' physiological status, posture changes, and fire scene conditions. The system incorporates ErgoLAB wireless ECG and PPG sensors, the BWT61CL posture sensor, and the MAG14mini infrared camera, which are capable of simultaneously capturing key physiological data and heat distribution in the fire scene. To ensure data accuracy and real-time performance, the study employs efficient signal preprocessing techniques, including noise reduction, baseline correction, and time synchronization. In addition, wireless transmission technology and multimodal data fusion algorithms are utilized to comprehensively analyze the firefighters' status and fire scene conditions. This approach significantly enhances the precision of firefighter safety monitoring and operational efficiency, demonstrating substantial innovation and practical value.

Research on the Application of Digital Twin Technology in Equipment Maintenance

Digital twin technology is to make full use of the data such as physical model, sensor update, running history, integrate the multi-subject multi-physical quantity, multi-scale, multi-probability simulation process, complete the mapping in virtual space, thus reflect the whole life cycle process of the corresponding entity equipment. In this paper, the digital twin of a certain type of equipment system is studied to realize the operation condition monitoring, fault intelligent diagnosis and so on, so as to improve the maintenance efficiency and effect of the equipment.

Self-Organizing and Routing Approach for Condition Monitoring of Railway Tunnels Based on Linear Wireless Sensor Network.

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network's lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads' load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs.

Впровадження автоматизованих систем моніторингу мостових споруд в Україні: Світовий досвід та національні перспективи

ntroduction. Highway bridges are an important element of the transport infrastructure of any country, and Ukraine is no exception. Their safe operation is critical to ensure the sustainable functioning of logistics systems and economic development of regions. However, due to constant loads, the impact of natural factors and insufficient funding for maintenance, a significant number of bridges in Ukraine are in poor technical condition. Accordingly, it is necessary to take measures to diagnose the condition of bridges in a timely manner and prevent possible accidents. In this context, automated condition monitoring systems (ACMS) are an effective tool for ensuring the smooth operation of such structures. Problem Statement. There are more than 16 thousand bridges on public roads of national importance in Ukraine, more than half of which do not meet modern standards for dimensions and load capacity. The problem is particularly acute for bridges that have been in operation for more than 40 years, as they are more prone to accidents. Due to the lack of systematic monitoring and control of the technical condition of such structures, there is a threat of their destruction. In the world practice, automated monitoring systems are actively used to prevent such situations. In Ukraine, the need to implement such systems is becoming increasingly relevant in the face of increased requirements for infrastructure security.

Wind turbine condition monitoring dataset of Fraunhofer LBF

Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases and measurement parameters.

Observation and Prediction of Instability due to RD Fluid Force in Rotating Machinery by Operational Modal Analysis

In recent years, as rotating machinery has become smaller and more efficient, various types of shaft vibration problems have arisen. Failure of rotating machinery may lead to major accidents and infrastructure shutdowns. Therefore, to prevent failures of rotating machinery, there is a growing need for the vibration analysis technology at the design stage and condition monitoring during operation stage. One of causes of the shaft vibration problems in rotating machinery is the rotordynamic (RD) fluid force acting on fluid elements such as journal bearings, seals, turbine blades, and so on. RD fluid force has a significant effect on the stability of rotating machinery and can destabilize the system. In recent years, operational modal analysis (OMA) methods, which identify modal parameters based on the measured data of a machine's operational condition, have been investigated in the condition monitoring. In this paper, the estimation of the modal parameters of rotating machinery using OMA from only the time history response of displacement data and, in particular, the prediction of the destabilization of rotating machinery caused by RD fluid force are investigated. As a result, the modal parameters are well estimated and, in particular, the destabilization of one mode due to RD fluid force is predicted and explained. The results are in good agreement with the results of the eigenvalue analysis of the original system, and the method is validated. Furthermore, the proposed method is applied to experimental data of the system destabilized by fluid force. The change in stability with rotational speed is observed, and the characteristics of the mode toward destabilization are confirmed. The results show the validity of OMA's predictions of destabilization in the experiments.

Bicuspid Valve Aortopathy: Is It Reasonable to Define a Different Surgical Cutoff Based on Different Aortic Wall Mechanical Properties Compared to Those of the Tricuspid Valve?

In this study, we examined and compared ex vivo mechanical properties of aortic walls in patients with bicuspid (BAV) and tricuspid (TAV) aortic valve aortopathy to investigate if the anatomical peculiarities in the BAV group are related to an increased frailty of the aortic wall and, therefore, if a different surgical cutoff point for ascending aortic replacement could be reasonable in such patients. Ultimate stress tests were performed on fresh aortic wall specimens harvested during elective aortic surgery in BAV (n. 33) and TAV (n. 77) patients. Three mechanical parameters were evaluated at the failure point, under both longitudinal and circumferential forces: the peak strain (Pstr), peak stress (PS), and maximum elastic modulus (EM). The relationships between the three mechanical parameters and preoperative characteristics were evaluated, with a special focus on evaluating potential risk factors for severely impaired mechanical properties, cumulatively and comparatively (BAV vs. TAV groups). The patient populations were inhomogeneous, as BAV patients reached surgical indication, according to the maximum aortic dilatation, at a younger age (58 ± 15 vs. 64 ± 13; p = 0.0294). The extent of the maximum aortic dilatation was, conversely, similar in the two groups (52 ± 4 vs. 54 ± 7; p = 0.2331), as well as the incidences of different phenotypes of aortic dilatation (with the ascending aorta phenotype being the most frequent in 81% and 66% of the BAV and TAV patients, respectively (p = 0.1134). Cumulatively, the mechanical properties of the aortic wall were influenced mainly by the orientation of the force applied, as both PS and EM were impaired under longitudinal stress. An age of >66 and a maximum dilatation of >52 mm were shown to predict severe Pstr reduction in the overall population. Comparative analysis revealed a trend of increased mechanical properties in the BAV group, regardless of the position, the force orientation, and the phenotype of the aortic dilatation. BAV aortopathy is not correlated with impaired mechanical properties of the aortic wall as such. Different surgical cutoff points for BAV aortopathy, therefore, seem to be unjustified. An age of >66 and a maximum aortic dilatation of >52 mm, however, seem to significantly influence the mechanical properties of the aortic wall in both groups. These findings, therefore, could suggest the need for more accurate monitoring and evaluation in such conditions.

Bearing fault detection in adjustable speed drives via self-organized operational neural networks

AbstractAdjustable speed drives (ASDs) are widely used in industry for controlling electric motors in applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of ASD-fed induction machines is very critical for preventing failures. Motor current signature analysis offers a non-invasive approach to assess motor condition. Application of conventional convolutional neural networks provides good results in detecting and classifying fault types for utility line-fed motors, but the accuracy drops considerably in the case of ASD-fed motors. This work introduces the use of self-organized operational neural networks to enhance the accuracy of detecting and classifying bearing faults in ASD-fed induction machines. Our approach leverages the nonlinear neurons and self-organizing capabilities of self-organized operational neural networks to better handle the non-stationary nature of ASD operations, providing more reliable fault detection and classification with minimal preprocessing and low complexity, using raw motor current data.

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.