Browse Topic: Maintenance, repair and overhaul (MRO)

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A Parametric Cradle-to-Grave Life Cycle Assessment Tool for Comparative Evaluation of Passenger Car CO₂ Emissions2026-37-0040To be published on 06/09/2026
This work presents the development of a flexible and user-oriented software tool for the cradle-to-grave Life Cycle Assessment (LCA) of passenger cars, enabling robust comparisons of greenhouse gas emissions across heterogeneous vehicle configurations. The tool supports informed decision-making by quantifying and visualizing environmental impacts associated with alternative mobility choices over the full vehicle life cycle, including production, use, maintenance, and end-of-life stages. A key contribution of the proposed framework is its high degree of configurability and readability. All parameters describing the vehicle and its usage can be freely modified, including powertrain type, dimensions and weight, ownership profile (new or second-hand vehicles, partial ownership periods, leasing scenarios), annual mileage, vehicle lifetime assumptions, and the carbon intensity of fuels or electricity sources. Country-specific energy mixes are explicitly considered, allowing the same vehicle
Gastaldi, Chiara, Cibrario, Luca
Transforming Aircraft MRO with Agentic AI and Context Engineering2026-26-0788To be published on 06/01/2026
Aircraft Maintenance, Repair, and Overhaul (MRO) operations are highly complex, involving coordination among multiple stakeholders including airlines, MRO providers, OEMs, and regulatory authorities. A significant challenge in this space is managing unplanned events such as Aircraft on Ground (AOG) conditions, where delays can lead to major financial losses to airlines and safety risks. Engineers must quickly diagnose the damage, evaluate compliance against regulatory limits, coordinate with OEMs, and make critical decisions—all while navigating a fragmented ecosystem of disconnected systems, diverse document types, and time-sensitive processes. This paper presents a real-world, intelligent MRO solution that addresses these challenges through the use of Agentic AI and context engineering. The system is designed to automate and augment key MRO workflows such as damage detection, repair pathway selection, compliance verification, and supplier coordination. At its core, the solution is
Abburu, Sunitha, G.V.V., Ravi Kumar, Poovalingam, Sundaresan, Vaderahobli, Devaraja Holla
Hybrid Digital Twin for Aero-Engine Bearing Prognostics: A Physics-Informed Machine Learning Approach to RUL Prediction2026-26-0718To be published on 06/01/2026
Unscheduled maintenance due to the failure of critical components, such as aero-engine rolling element bearings, is a leading cause of costly Aircraft-on-Ground (AOG) events; consequently, current time-based maintenance practices are inefficient and prone to risk. This paper develops a resource-efficient Hybrid Digital Twin (HDT) model for an engine bearing, focusing on the dynamic prediction of spall growth due to Rolling Contact Fatigue (RCF), thereby enabling a condition-based maintenance paradigm. The HDT architecture integrates two core models: (1) a physics-informed model that uses established life and fatigue theory to define initial degradation thresholds, and (2) a data-driven Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, for dynamic degradation rate modeling. The methodology utilizes a Monte Carlo simulation coupled with RCF progression equations to generate a large, high-fidelity synthetic run-to-failure dataset under varying
Mohamed, Abbas
Retrofit Vibration Mitigation of Launch Vehicle Structures Using Thin Viscoelastic Coatings: Acoustic and Modal Study2026-26-0736To be published on 06/01/2026
Acoustic-induced vibrations pose a significant risk to launch vehicle hardware and payload reliability during critical phases such as lift-off and transonic phase. Reducing such vibrations is especially challenging when the hardware has already been fabricated, limiting the possibility of structural redesign. This study demonstrates a practical post-fabrication solution using a thin viscoelastic polymer coating applied externally to fully assembled hardware. Comprehensive evaluations were conducted using both acoustic testing and Experimental Modal Analysis (EMA) before and after coating application. During acoustic test, a substantial decrease in structure response from 150Hz to 2000Hz, with a reduction of approximately 50% in the grms values was observed for the coated structure demonstrating significant vibration mitigation over a wide frequency range. In contrast, EMA measurements using impact excitation revealed that the response transfer functions did not show a significant
Avirah, Nohin K, Panda, Ajay Kumar, Shaikh, Altafhusen
Aircraft Cabin Diagnosis – Inspection of the Aircraft Cabins and Seats Using AI2026-26-0789To be published on 06/01/2026
Aircraft interior defects, including seat structural damage, cushion degradation, liquid contamination, and foreign object presence, contribute to increased maintenance burden, extended ground time, and operational inefficiencies. Current inspection practices rely predominantly on manual visual checks, which are time-intensive and limited in detecting concealed anomalies. This paper presents a non-contact, AI-enabled inspection framework integrating millimeter-wave (mmWave) radar sensing with high-definition optical imaging for automated aircraft seat condition assessment. The proposed system captures interior scans when the aircraft is unoccupied and compares them against a digitally established baseline reference obtained under certified, defect-free conditions. Data fusion and machine learning algorithms analyze deviations to identify surface and subsurface defects at seat-level resolution and generate zone-based maintenance maps. The primary technical contribution lies in combining
Nagoal, Chandrasekhar Reddy, Prathipati, Krishna Chaitanya, Kandukuri, Ravindra
Enabling Circularity in the Aerospace Maintenance Ecosystem through Generative-AI Driven Back-to-Birth Traceability of Life-Limited Engine Parts2026-26-0719To be published on 06/01/2026
Circular-economy principles are increasingly central to aerospace sustainability strategies, aiming to extend asset life, improve asset valuations, and enhance benefits to stakeholders in the part ownership and maintenance lifecycle. In aircraft engines, achieving circularity hinges on safe reuse, repair, and recirculation of high-value components. Life-Limited Parts (LLPs) are among the most critical in this context, but their reuse is strictly contingent on complete Back-to-Birth (BtB) traceability. Any gap in BtB records—often due to fragmented data across multiple airline operators, shop visits, document formats, and time expanse—renders otherwise serviceable LLPs unusable, leading to premature scrappage and lost circular value. This paper presents a Generative AI (GenAI)-driven methodology to reconstruct and validate complete LLP BtB histories from heterogeneous, unstructured, and legacy maintenance datasets. By combining aerospace domain-trained language models with embedded life
Bhate, Ujwal, Jain, Dilip Kumar, Kulkarni, Ninad, Kalaiyarasan, Aravindh, Jha, Ashish, Shenoy, Karthik
Design and Run-Time Information Exchange for Health-Ready ComponentsJA6268_202303 (Current)5/26/2026
This Surface Vehicle & Aerospace Recommended Practice offers best practices and a methodology by which IVHM functionality relating to components and subsystems should be integrated into vehicle or platform level applications. The intent of the document is to provide practitioners with a structured methodology for specifying, characterizing and exposing the inherent IVHM functionality of a component or subsystem using a common functional reference model, i.e., through the exchange of design-time data and the application of standard vehicle data communications interfaces. This document includes best practices and guidance related to the specification of the information that must be exchanged between the functional layers in the IVHM system or between lower-level components/subsystems and the higher-level control system to enable health monitoring and tracking of system degradation severity. The intent is to provide an IVHM system that can robustly report the degradation of a given
HM-1 Integrated Vehicle Health Management Committee
Research on Fault Diagnosis Agent of Monorail Crane Based on Knowledge Graph and Lightweight Large Model2026-99-07415/15/2026
The monorail crane is important in mining operations, and its operation affects both safety and efficiency. Currently, fault diagnosis for monorail cranes has several challenges, such as heterogeneous mixing of multimodal data, poor use of knowledge, low real-time requirements, and high deployment costs for large-scale models. To solve these problems, we present an agent framework using a multimodal knowledge graph and a lightweight large model. In particular, we construct a fault knowledge graph for monorail cranes, organizing professional knowledge about components, failure modes, symptoms, and maintenance. By employing retrieval-augmented generation (RAG) technology, the knowledge graph is merged with the Qwen lightweight large model (low-rank adaptation) for fine-tuning to develop a diagnostic agent with task planning, tool invocation and memory. The experimental results show that the agent framework reduces “machine hallucination” and outperforms conventional diagnostic accuracy
Zhang, Yixuan, Xue, Shun, Bi, Xiang, Wei, Xing, Kang, Ranyu, Jue, Jie, Cheng, Liruiran
Fire Prevention on Forestry MachineryJ1212_202605 (Current)5/7/2026
This document applies to off-road forestry work machines defined in SAE J1116 or ISO 6814.
MTC4, Forestry and Logging Equipment
Can Multi-Fingered Robots Transform Shipboard Operations and Autonomous Maintenance?26AERP05_125/1/2026
USC Viterbi researcher received Office of Naval Research's Young Investigator Program award with Study on dexterous robotics. University of Southern California, Los Angeles, CA In dynamic, unstructured environments like ship decks and even home kitchens, robots today still struggle to perform precision tasks such as tightening bolts or handling wires. This makes critical ship maintenance tasks difficult. USC researcher, Erdem Bıyık, aims to advance robots' finger manipulation and integrate human feedback to enable real-time learning for robots in an upcoming three-year, $750,000 project funded by the Office of Naval Research (ONR).
Advanced Wheel Attachment Loosening Identification through Algorithmic Gear Error Analysis and Probabilistic Assessment2026-01-05904/7/2026
At present, tire failures directly affect road safety, and the number of incidents caused by them is gradually increasing. Examining wheel attachment loosening on time is vital for vehicle safety. Tire-related incidents not only put people in peril but also have a detrimental effect on the economy. Therefore, the goal of this research is to develop a new and effective method for identifying wheel attachment loosening. A novel gear error reduction approach, distinct from traditional methods, combines advanced computing and probabilistic analysis. This paper involves three key components: extracting looseness eigenvalues, calculating ring gear errors, and computing the tire loosen probabilities. Gear errors derived from the Kalman filter and adjusted for speed, eigenvalues were calculated, and a tire loosening probability analysis was performed. Real-car trials across speeds and roads confirm its accuracy and reliability. This technology can improve automotive safety and maintenance
Liu, Jianjian, Zhang, Zhijie, Wang, Zhenfeng, Ma, Guangtao, Shi, Meijuan, Liu, Jing, Zhao, Binggen, Lu, Yukun
Enabling Sustainable E-Mobility: An Edge–Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles2026-01-04604/7/2026
The rapid adoption of electric vehicles (EVs) is a cornerstone of the transition to sustainable transportation. However, uncertainty regarding battery degradation remains a significant obstacle, hindering vehicle energy efficiency, operational safety, and the recovery of end-of-life value. Accurate estimation of the battery state of health (SOH) and prediction of the remaining useful life (RUL) are therefore critical for sustainable vehicle lifecycle management. This study proposes an edge–cloud collaborative intelligent framework for in-vehicle deployment that leverages a Transformer-based architecture to jointly model SOH and RUL. The cloud-side model retains the full configuration to capture long-term degradation trajectories for high-accuracy RUL prediction. A lightweight edge-side model, engineered via pruning and knowledge distillation, delivers millisecond-level inference for real-time SOH estimation onboard the vehicle. To ensure efficiency, only four core health indicators are
Gao, Weimin, Lv, Zhilong, Ou, Shiqi(Shawn)
Development and Application of SAE J3260 & J3290 Recommended Practice for Minimizing Spread of Infections in Motor Vehicles2026-01-01284/7/2026
The onset of the COVID-19 pandemic in early 2020 introduced an unprecedented disruption to global industries, including automotive service and maintenance. As technicians and service shops struggled to balance operational continuity with safety, uncertainty surrounded best practices for servicing potentially dangerous vehicle cabins and air conditioning systems. This paper traces the evolution of these early efforts, from initial confusion and informal guidance to the establishment of the SAE Cabin Disinfection Practices Committee (SAE TEVCDPC) and the eventual publication of SAE J3260 and SAE J3290. It also considers work done by ASHRAE (the American Society of Heating, Refrigerating and Air-Conditioning Engineers), which simultaneously worked on ASHRAE Standard 62.1 and 241. These standards, along with contributions from subject matter experts, formalized the automotive industry’s response to infection control in vehicle environments, integrating scientific understanding with
Schaeber, Steven, Mathur, Gursaran, Taylor, Dwayne
An Efficient Approach to Key Negotiation in an Automotive Environment2026-01-00874/7/2026
Negotiating Keys for applications such as message authentication within a vehicle presents many problems as, in designing the algorithm; the algorithm must be able to be utilized by small, fixed-point processors. In addition, if there is a desire to do this algorithm in the manufacturing environment, there are severe time constraints placed on how long this algorithm can take, as there are strict station time requirements, which are expensive to change, and any time utilized in the plant can negatively affect vehicle throughput. Additionally, negotiating these keys between many ECUs can greatly increase the time required to negotiate a common key using standard multi-party Diffie-Hellman. Timing would also be an issue in the case of using pair-wise Diffie-Hellman for encryption and distribution of keys utilizing a key master. To solve these problems in multi-party key negotiation, we have utilized the Elliptic Curve variation of the Burmester-Desmedt (ECBD) algorithm. ECBD is
Van Dam, Theo, Mazzara, Bill
Integrating Anti-Idle Power Systems into Military Tactical Vehicles: Use Case Development and Analysis2026-01-50203/11/2026
Military tactical vehicles are increasingly incorporating anti-idle kits as a method to reduce fuel consumption. The larger battery pack associated with the anti-idle kit has the potential to provide new capabilities to the warfighter, who can use the battery pack to power pieces of equipment. This study analyzes a set of these new capabilities derived from the U.S. Army Universal Task List, supplemented with user interviews and doctrinal analysis. These capabilities include powering dismounted soldier systems, counter-drone and surveillance equipment, mobile refrigeration for medical applications, field maintenance tools, and mobile food services. The study then uses geolocation data collected from the U.S. Army’s National Training Center to model daily fuel consumption for soldiers performing each of these activities. The model was subsequently adapted to incorporate an anti-idle kit, revealing significant reductions in fuel usage. The analysis uses the results to define common
Lusian, Trevonte, Mummert, Taige, Kaiser, Caleb, Greer, Michael, Black, Nathaniel, Ong, Bennett, Tapahonso, Eugene, Mittal, Vikram
Technical Manual Requirements for Ground Support EquipmentAS4828A (Current)2/12/2026
This document is a guideline for format, structure and content for ground support equipment (GSE) technical manuals. This document focuses on requirements specific to the GSE industry and does not cover general technical publication practices. Additional standards for GSE and for manufacturer’s publications exist and may add requirements beyond what is covered in this standard. This may include EU Directive 2006/42/EC. This document is written in specific terms by intention, and conforms to recognized practices in the industry. When the word SHALL is used in this standard, it indicates a requirement that must be adhered to in total and does not allow for variance. When the word SHOULD is used, it indicates a recommended practice which allows the manual writer to use discretionary judgment. This document does not apply to electronic test equipment.
AGE-3 Aircraft Ground Support Equipment Committee
GSE Training VideosAIR4730A (Current)2/12/2026
This document provides information on the preparation and use of video for operational and maintenance training of qualified personnel associated with GSE.
AGE-3 Aircraft Ground Support Equipment Committee
Optimisation-Driven Framework for Heterogeneous Battery-Pack Mixing in Fleet Applications2026-26-02091/16/2026
Fleet owners often encounter significant logistical and financial problems when dealing with battery packs of different ages and conditions. The standard industry practice is to replace old batteries with identical new ones. This process is inefficient because it costs a lot, creates too much inventory, and eliminates battery packs that are still useful too soon. The problem worsens when manufacturers stop making older battery models, which can force a vehicle to retire early. This paper puts forward a framework for mixing different types of battery packs to deliver the performance needed for a vehicle’s mission. We show how this works in three everyday service situations: 1) Repair, when a single damaged pack needs replacing; 2) Life Extension, where aged packs are combined with newer ones to meet mission range; and 3) Performance Restoration, which uses next-gen packs when the original parts are obsolete. The study shows that a vehicle can complete its required missions by
Nair, Sandeep R., Ravichandran, Balu Prashanth, Hallberg, Linus
Self-Supervised Diagnostics & Prognostics Framework for Predictive Maintenance in Electric Vehicles2026-26-06731/16/2026
Predictive maintenance is critical to improving reliability, safety and operational efficiency of connected vehicles. However, classic supervised learning methods for fault prediction rely heavily on large-scale labeled data of failures, which are difficult to obtain and maintain a manually built dataset of failure events in real automotives settings. In this paper, we present a novel self-supervised anomaly detection model that makes predictions on the faults without the need for labeled failures by using only the operational data when the systems or robots are healthy. The method relies on self-supervised pretext tasks, like masked signal reconstruction and future telemetry prediction, to extract nominal multi-sensor dynamics (i.e., temperature, pressure, current, vibration) while jointly minimizing the deviation between encoded/decoded signals and normal patterns in the latent space. A unsupervised anomaly detection model is then used to detect when the learned patterns are violated
Kumar, Pankaj, Deole, Kaushik, Hivarkar, Umesh
Enhancing Signal Integrity and Security in Aerospace: RF-Over-Fiber Optics Hybrid Communications TechnologyTBMG-544351/1/2026
Without reliability and signal integrity, aerospace communications risk severe signal degradation and reduced security, posing risks to both personnel and mission-critical data. These challenges are particularly critical for applications that depend on military aircraft, satellite communications, and unmanned aerial vehicles (UAVs). As global demand for real-time data continues to surge, communication infrastructure requires regular maintenance and upgrades to maintain secure and reliable performance.
Research Progress on Pavement Performance Prediction Models: A Review2025-99-024912/23/2025
Road maintenance plays a vital role in maintaining road conditions and ensuring safety, especially in a country with an extensive road network like China. To accurately predict pavement performance, optimize maintenance strategy, reduce cost and improve road efficiency, the paper systematically combed and evaluated the prediction model of pavement performance. Firstly, the importance of pavement maintenance and the background of pavement maintenance performance prediction model are described, and explicit models (mechanical-empirical model, stochastic process, time series analysis) and machine learning models (regression analysis, support vector machine, integrated learning, artificial neural network, deep learning) are introduced respectively. The basic principle, representative study, advantages and disadvantages of each model are introduced in detail. Comparative analysis shows that the traditional explicit model is simple and effective, easy to explain, but difficult to deal with
Ma, Muyun, Dong, Qiao, Lin, Yelong
Research on Energy Consumption Simulation and Carbon Emission Simulation in the Rail Transit Operation and Maintenance Phase Based on DesignBuilder: A Case Study of Tongyuan Road South Station on Suzhou Metro2025-99-022212/23/2025
At present, the rail transit network in China is well-developed and has become an important means of daily travel for residents. Rail transit stations usually achieve seamless connections with other transportation modes such as buses, taxis, and shared bicycles. It will evolve into an integrated transportation hub, effectively alleviating the pressure on urban surface transportation and playing a pivotal role in dispersing a large number of commuters. Meanwhile, with the vigorous development of rail transit, its energy consumption is increasing. It results in considerable carbon emissions, which poses a huge challenge to China’s goal of achieving carbon neutrality by 2030. In this paper, the building energy consumption simulation tool DesignBuilder is used to model the Tongyuan Road South Station of Suzhou Rail Transit. The energy consumption generated during its operation stage is simulated, and the carbon emissions produced by Tongyuan Road South Station at this stage are calculated
Zhu, Ning
A Literature Review on Application of Maintenance and Management Strategies for Electric-Vertical Take-Off and Landing Aircraft’s Maintenance, Repair, and Overhaul Organizations01-18-03-001612/18/2025
In the context of emerging technology developed for advanced air mobility concept, its maintenance protocols are not yet mature and existing aviation maintenance systems may not support electric-vertical take-off and landing (e-VTOL) needs. Thus, the operation of e-VTOL aircraft during its deployment stage necessitates the need for qualitative maintenance support. The main purpose of this study is to develop the basic structural principles of the projected new maintenance, repair, and overhaul (MRO) organization for e-VTOL air vehicles, which will support airworthiness through comprehensive maintenance approaches. Thus, the operation of e-VTOL aircraft during its deployment stage necessitates the need for qualitative maintenance support. The importance of the study is to offer standard procedures based on management and maintenance strategies, application of predictive and prescriptive maintenance tools, which pose a significant contribution to ensuring safety, reliability, and cost
Imanov, Tapdig, Bozdereli, Arzu
Efficient Tunnel Modeling in Unity3D with Data-Driven CSG Approach2025-99-034912/17/2025
Tunnels are vital infrastructures in daily life. To utilize digital twin technology for more efficient and convenient tunnel operation and maintenance, tunnel modeling serves as its foundation. However, existing tunnel modeling methods always suffer from high computational complexity, poor generalizability, and low expressive efficiency. This article proposes a data-driven tunnel modeling approach based on the Unity3D platform. Based on the actual engineering drawings, the method obtains the tunnel parameter set through the classification and feature analysis of the tunnel structure. A process-oriented model representation, i.e. a Constructive Solid Geometry (CSG) tree is then employed, enabling the creation of portal models without dependence on specific data structures. Meanwhile, the mesh optimization idea of downward triangulation and the neighbor-edge detection mechanism are introduced to improve the expression efficiency while maintaining the integrity and correctness of the
Wu, Jianjie, Luo, Xingyu, Mei, Hongliang, Lu, Yuxiang, Wang, Zhiyuan, Chen, Weiya
Experimental Study on Key Technologies of Prefabricated Fuel Hydrant Well2025-99-036312/17/2025
With the rapid development of the aviation industry, there is an increasing demand for safe apron operations and support capabilities. As a key facility in the apron fuel supply pipeline network, the performance and stability of the fuel hydrant well are crucial. However, the traditional repair and replacement process for fuel hydrant wells faces challenges, including lengthy construction times and significant impacts on airport operations. To address these issues, this article proposes a prefabricated refueling hydrant well technology, aimed at achieving rapid replacement of hydrants under non-stop construction conditions. Through on-site experiments, we have verified the feasibility of this prefabricated fuel hydrant well technology, determined the minimum dismantling boundary, and studied the rapid dismantling process, prefabricated pavement structure and installation process, as well as the application of self-compacting and fast-setting high-strength wellbore filling materials
Ren, Yucheng, Zhao, Kunyang, Chang, Lingsu, Wang, Xiangjun, Han, Tianhui, Li, Zonghe
Construction of an Intelligent Decision-Making System for Pavement Maintenance of Gansu Highway Based on DeepSeek2025-99-045612/10/2025
Pavement maintenance decision-making is the key to determining the maintenance program and ensuring the maintenance effect. Still, the existing pavement maintenance decision-making methods have problems, such as incomplete and inaccurate data. Based on this, this study develops an intelligent decision-making system for pavement maintenance on highways in Gansu Province by combining DeepSeek artificial intelligence technology with dynamic capability theory. The proposed framework integrates multi-source data fusion, predictive analytics, and organizational collaboration mechanisms to address the systematic challenges of resource allocation and decentralized decision-making. A spatio-temporal graph convolutional network enables accurate pavement performance modelling, while a redesigned decision-making process enhances cross-departmental coordination through game-theoretic optimization and blockchain-based traceability. The results show significant improvements in operational efficiency
Xie, Zilong, Liu, Chunya, Huang, Tao, Kou, Yujiao, Xie, Binglei, Xue, Xue
Robotic System for Assembly and Maintenance of Lightweight Reconfigurable StructuresTBMG-5428212/1/2025
Missions to the moon and other planets will require large-scale infrastructure that would benefit from autonomous assembly by robots without on-site human intervention. Modular and reconfigurable structures, such as those built from lattice-based building blocks, are reusable and easy to manufacture. Furthermore, reconfigurable systems have the potential to outperform traditional, fixed infrastructure in applications that require high levels of flexibility in addition to structural strength and rigidity. NASA Ames Research Center has developed a novel and efficient mobile bipedal robot system to construct low-mass, high precision, and largescale infrastructure.
Optimization System of E-commerce Logistics Distribution Path Based on Data Analysis2025-99-010811/11/2025
With the rapid development of e-commerce, the logistics industry also presents new features such as multi-level, integrated upstream-downstream operations, increasingly perfect service quality and low logistics costs. The exponential growth in online transactions and consumer expectations for faster, more reliable deliveries intensifies the pressure on logistics systems. The last-mile service network refers to the logistics nodes that have direct contact with consumers, and its geographical location and quantity will directly affect the service level, cost and customer service mode of the distribution network. However, with the rapid growth in the number of online shoppers and their imbalance on the Internet, these factors have gradually become an important basis for influencing the layout of terminal outlets. This imbalance, coupled with dynamic urban traffic conditions, renders traditional distribution planning methods inadequate. Therefore, in the e-commerce environment, how to
Tong, Tong, Gu, Xuefei, Li, Lingxiao
Connected Aftermarket Services for Off-Highway Vehicles: Enhancing Fleet Management Through Real-Time Data and Predictive Maintenance2025-28-030711/6/2025
Off-highway vehicles (OHVs) are essential in heavy-duty industries like mining, agriculture, and construction, as equipment availability and efficiency directly affect productivity. In these harsh settings, conventional maintenance plans relying on set intervals frequently result in either early component replacements or unexpected breakdowns. This document presents a Connected Aftermarket Services Platform (CASP) that utilizes real-time data analysis, predictive maintenance techniques, and unified e-commerce functionalities to evolve OHV fleet management into a proactive and smart operation. The suggested system integrates IoT-enabled telematics, cloud-based oversight, and AI-powered diagnostics to gather and assess machine health indicators such as engine load, vibration, oil pressure, and usage trends. Models for predictive maintenance utilize both historical and real-time data to produce advance notifications for component failures and maintenance requirements. Fleet managers get
Vashisht, Shruti
E-Motor Thermal Management Using 2D-3D Coupling2025-28-022511/6/2025
In the electrical machines, detrimental effects resulted often due to the overheating, such as insulation material degradation, demagnetization of the magnet and increased Joule losses which result in decreased lifetime, and reduced efficiency of the motor. Hence, by effective cooling methods, it is vital to optimize the reliability and performance of the electric motors and to reduce the maintenance and operating costs. This study brings the analysis capability of CFD for the air-cooling of an Electric-Motor (E-Motor) powering on Deere Equipment's. With the aggressive focus on electrification in agriculture domain and based on industry needs of tackling rising global warming, there is an increasing need of CFD modeling to perform virtual simulations of the E-Motors to determine the viability of the designs and their performance capabilities. The thermal predictions are extremely vital as they have tremendous impact on the design, spacing and sizes of these motors.
Singh, Bhuvaneshwar, Tirumala, Bhaskar, Badgujar, Swapnil, HK, Shashikiran
Eyes of Robot Operating System for Future Hands2025-28-024611/6/2025
This paper presents a novel approach to automated robot programming and robot integration in manufacturing domain and minimizing the dependency on manual online/offline programming. Traditional industrial robots programming is typically done by online programing via teach pendants or by offline programming tools. This presents a major challenge as it requires skilled professionals and is a time-consuming process. In today’s competitive market, factories need to harness their full potential through smart and adaptive thinking to keep pace with evolving technology, customer demand, and manufacturing processes. This requires ability to manufacture multiple products on the same production line, minimum time for changeovers and implement robotic automation for efficiency enhancement. But each custom automation piece also demands significant human efforts for development and maintenance. By integrating the Robot Operating System (ROS) with vision-based 3D model generation systems, we address
Hepat, Abhijeet
Enhancing Diagnostic Efficiency and Customer Support for Off-Highway Vehicles in India2025-28-030111/6/2025
Off-highway vehicles (OHVs) are vital for India’s construction, mining, agriculture, and infrastructure sectors. With growing demand for productivity and sustainability, the need for efficient customer support and precise diagnostic techniques has become paramount. This paper presents a comprehensive study of challenges faced in India, current and emerging diagnostic technologies, troubleshooting techniques, and strategies for effective customer support. Case studies, tables, and diagrams illustrate practical solutions.
Mulla, Tosif, Thakur, Anil, Tripathi, Ashish
Predictive Maintenance Solution with Real-Time Insights and Cost Estimation for Machine Health Management2025-28-030211/6/2025
This paper introduces a comprehensive solution for predictive maintenance, utilizing statistical data and analytics. The proposed Service Planner feature offers customers real-time insights into the health of machine or vehicle parts and their replacement schedules. By referencing data from service stations and manufacturer advisories, the Service Planner assesses the current health and estimated lifespan of parts based on metrics such as days, engine hours, kilometers, and statistical data. This approach integrates predictive analytics, cost estimation, and service planning to reduce unplanned downtime and improve maintenance budgeting, aligning with SAE expectations for review-ready manuscripts. The user interface displays current part health, replacement due dates, and estimated replacement costs. For example, if air filter replacement is recommended every six months, the solution uses manufacturer advisories to estimate the remaining life of the air filter in terms of days or
Chaudhari, Hemant Ashok
Analysis of Maintenance Strategies of Electrical Systems of Electric Bus Charging Station2025-28-040710/30/2025
Charging management has a profound impact on the reliability and safety of electric bus (EB) services. However, the actual charging operation of EB fleets is a critical challenge due to uncertain energy consumption, limited charging resources and other factors. At present there are no operational and maintenance guidelines present for operation of EB charging stations since the running and operation of these facilities are at the initial stages of development. There is a need to develop these strategies that provides smooth operation of these newly developed facilities. In the present work maintenance strategies of electrical systems of Electric bus charging station were designed. The complete maintenance is divided into quarterly and annual maintenance based on the requirements and nature of work for smooth operation. Quarterly Maintenance is devised on detecting early signs of wear through visual inspections of key components, including transformers and ventilation systems while
Soam, Kumaresh, Vashist, Devendra
Decay Analysis of Xinjiang G577 Asphalt Road Performance Based on Exponential Modeling2025-99-005410/17/2025
The accurate prediction of road performance decay is of great significance for road maintenance and management. This paper takes the Xinjiang G577 highway as the research object, collects the measured data of the typical indexes of asphalt pavement since the past years (Deterioration Condition Index (PCI), Technical Condition Index (PQI)), and studies its decay. The model is constructed on the basis of time series1, and the exponential decay model of asphalt road PQI and PCI is derived. The model’s accuracy is then tested by calculating the correlation coefficient, mean absolute error (MAE), and other accuracy tests. The results demonstrate that the model exhibits a high degree of fit.
Tian, Wei, Bai, Haotian, Wang, Taiwei, Wang, Jiayan, Dai, Xiaomin
High-Precision Simulation of Asphalt Pavement Service Performance and Development of Intelligent Maintenance Platform2025-99-003110/17/2025
During the service life of asphalt pavement, its performance degrades rapidly, causing high maintenance costs. This paper gathers over 100,000 traffic data records from Guangxi and uses a spatiotemporal multi-scale data deep learning algorithm to simulate and predict the future pavement technical condition for the next 20 years. In the validation set, the mean PA values of the PCI and RQI service performance indicators exceed 90%, with P90 values also over 90%. This overcomes the limitations of existing methods in terms of low accuracy and high subjectivity. Based on this, a complete intelligent preventive maintenance platform has been developed, integrating multiple functions. During the service period, the average pavement performance enhanced by 14%. This platform simplifies maintenance operation, cuts costs and extends service life.
Feng, Xuemao, Wang, Hongwei, Han, Geng, Li, Wenrui
Study on Characterization of Pavement Performance Evolution Based on Accelerated Loading Test System (RIOHTrack)2025-99-004210/17/2025
To evaluate the performance evolution patterns of road structures under natural environmental conditions and loading, data were collected from the RIOHTrack system. Pavement deflection, smoothness, and skid resistance were selected as evaluation indicators. The performance evolution characteristics over 50 million load cycles were analyzed to investigate the impact of different structural configurations on service performance. The study results are summarized as follows: The deflection basin area exhibits significant annual cyclic fluctuations, indicating that ambient temperature significantly affects pavement deflection. The initial rapid decrease in texture depth was attributed to the compaction of the surface layer under traffic loading, leading to a reduction in texture depth. Differences in tire and subgrade stiffness can cause variations in texture depth across various scenarios. Circular pavement structures' smoothness can be categorized into three classes; however, even within
He, Yan, Li, Haibo, He, Chuanping, Zhang, Yangpeng, Ma, Qing, Li, Pengfei, Wang, Jie
Knowledge Graph Completion for Rail Transit Maintenance Based on Directed Subgraphs and Dynamic Information Flow2025-99-004910/17/2025
In order to deal with sparsity and incompleteness issues in the knowledge graph (KG) of urban rail transit operation and maintenance (O&M), this paper introduces a dynamic information flow based directed subgraph-based knowledge graph completion (KGC) method. Adding ontology constraints and semantic similarity calculations, the dynamic directed subgraph of new entities is constructed, enabling precise candidate entity and relation set selection, and successfully capturing contextually relevant domain information. Next, an embedding generation model with a dynamically updated information flow is constructed, integrating multi-layer message passing and self-attention mechanism to progressively obtain semantic features and structural dependencies from the subgraph and generate context-aware embeddings for entities and relations. Finally, the ConvE model acts as a decoder to learn higher-order entity and relation interactions in the triples and generate correct triple scores for efficient
Zhou, Lujie, Gao, Sai, Zhang, Haifei, Liang, Chaohui
SAE TOMORROW TODAY - Introducing "TeleHealth" Video Chats For Car Repair1353810/10/2025
Have you every wished you could call up an experienced auto tech to diagnosis your car trouble? One company is doing just that by transforming the DIY auto repair experience. With nearly 1 million app downloads, Tinker DIY is the only platform that offers live video support with ASE-certified mechanics for auto repairs, rideshare inspections, and used car evaluations. Akin to "telehealth for your car," Tinker eliminates the need for YouTube guesswork or costly auto shop visits by guiding users through step-by-step auto repairs in real-time. Listen in as we sit down with authors Megan Han, Head of Operations, to discuss how Tinker helps connects users with expert guidance to help them diagnose and repair their vehicles on their own. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the
Patterson, Lori
Remaining Useful Life prediction of Engine Fuel System Using Machine Learning2025-01-037710/7/2025
Reliability and uptime are critical priorities in the automotive industry, prompting a shift toward predictive maintenance (PdM) to minimize unexpected failures and associated costs. This study presents a machine learning-based framework for early prediction of engine fuel system failures using embedded field performance data. This study introduces a machine learning-based framework for predicting failures and estimating the remaining useful life (RUL) of mid-range diesel engines with high-pressure common rail fuel systems in vehicles using classification and regression models applied to embedded field performance analysis data, aiming to enhance reliability and reduce unplanned downtime. Two classification models --- Random Forest and XGBoost top our model metrics chart. They were further tuned and evaluated, with XGBoost achieving superior performance, including 94% accuracy and 87% precision, and a low false positive rate of 0.01, enabling an 8-day lead time for proactive
Wang, Tingting, Goswami, Anil, Akinola, Michael, Yang, Tina, An, Qi
3D Printed Implant Helps Repair Spinal Cord InjuriesTBMG-5399210/1/2025
A research team at RCSI University of Medicine and Health Sciences has developed a 3D-printed implant to deliver electrical stimulation to injured areas of the spinal cord offering a potential new route to repair nerve damage. Details of the 3D-printed implant and how it performs in lab experiments have been published in the journal Advanced Science.
3D Printed Cardiac Patch Helps Repair Heart DefectsTBMG-5399810/1/2025
ETH Zurich Zurich, Switzerland
Upstream: better data collection, processing will reduce SDV recalls25AUTP10_1210/1/2025
Cars that are more connected, equipped with more sensors than ever before, should make proactive maintenance somewhat easy and reliable. Drivers could have lower repair costs and fewer breakdowns overall if the automotive industry shifts away from a periodic maintenance mindset towards data-driven proactive services. But the automakers themselves would also win with a massive drop in recalls. A connected car requires accurate telemetry sensors to track details such as temperature (in the engine, battery, and cabin), pressure (in tires, fuel, and oil), electrical current/voltage, and vibration patterns to detect problems before they become failures. The vehicle also needs to be able to combine telemetry, diagnostics, metadata, and service records in one platform and then make sense of it all. “Poor data integration kills even the best analytics,” according to Upstream co-founder and CTO Yonatan Appel.
Blanco, Sebastian
Implementing Predictive MaintenanceTBMG-5390410/1/2025
Grease innovation adapts to meet new demands of heavy equipment25TOFHP10_0510/1/2025
In the commercial and off-highway sectors, equipment reliability isn't just a maintenance target but a business imperative. Whether it's a long-haul truck on the interstate or a dozer working through dust and rock, these machines operate in some of the most demanding environments on Earth. And while engine design and fuel choice often dominate conversations about performance, the role of grease is just as critical, particularly as equipment is pushed harder and longer under more variable conditions. Over the last decade, heavy-duty grease development has undergone a quiet evolution. Performance expectations have risen sharply. So have the environmental and regulatory considerations that influence formulation decisions.
Kumar, Anoop
Modernization of Integrated Technologies for Ground Systems (MINT-GS) – Screening OF 17-4PH SS Laser Powder Bed Fusion Additive Manufacturing Process2025-01-04939/16/2025
Additive Manufacturing is currently being utilized to improve military readiness by transforming maintenance operations and the supply chain associated with repairing or replacing parts or components on legacy vehicles. The National Institute for Aviation Research at Wichita State University is collaborating with the Army Ground Vehicle Systems Center in the creation of a rapid qualification framework for various additive manufacturing materials and processes to support the modernization and sustainment of ground vehicles. Currently, a rapid qualification 17-4PH stainless steel material is being executed utilizing Laser Powder Bed Fusion and Direct Energy Deposition additive manufacturing processes. Prior to entering the rapid qualification, pre-qualification screening studies are performed to select the feedstock and develop process control to limit risk within the qualification. An overview of the pre-qualification screening studies performed in selecting the feedstock and heat
Tomblin, John, Andrulonis, Rachael, Saathoff, Brandon, Thomas, Annika, Daharsh, Cole, Lowney, Matthew, Walker, Eric
Commander’s Decision Analysis Tool for Maintenance: Applications of Unsupervised Learning with Pattern Characterization (ULPC) in Vehicle Health Assessment2025-01-04909/16/2025
Within the military maintenance cycle, commanders and units struggle with understanding the operational readiness of their fleets from a data driven perspective. Many unsupervised learning techniques have been developed with applications for vehicle maintenance with pattern classification. In this paper, Predictive Maintenance using Unsupervised Learning with Pattern Characterization (ULPC) is proposed to classify the overall health of the platform system and subsystems. In this model, the key features are selected using an intelligent pre-processing system for signal classification for each subsystem. Next the data is processed and compared to a normalcy baseline dataset using the unsupervised machine learning (ML) model. Operational data collected post-baseline is then processed through a Recurrent Neural Network (RNN) and clustered. An overall “normalcy” metric is calculated to show the difference in operation when compared to the baseline patterns. This normalcy servers as an
Bailey, Jeffrey, Cabrey, Connor, Hsu, Charles
Reducing Range Prediction Uncertainty in Heavy-Duty Electric Vehicles Using a Route-Informed Transformer-Based Model2025-24-01229/7/2025
Electrification of heavy-duty on-road trucks used for regional freight transportation is a viable option for fleets to reduce operation and maintenance costs and lower their carbon footprint. However, there is considerable uncertainty in projecting their daily range because highly variable payload mass, among other factors, confounds battery state of charge (SOC) prediction algorithms. Previous work by the authors proposed an electric vehicle range prediction model based on two parallel recurrent neural networks (RNNs). The first RNN used mean-variance estimation to output a predicted mean and variance, and the second used bounded interval estimation to provide bounds on the SOC required to complete a trip. The dual RNN approach resulted in estimating the remaining range and error bands of the SOC over the route. The previous work was limited because it did not incorporate driving conditions, like road type and ambient temperature, that affect driver behavior and energy consumption
Jayaprakash, Bharat, Eagon, Matthew, Northrop, William F.
The VERT GPF-Retrofit Program for Cleaner Urban Mobility within the HORIZON Europe AeroSolfd Project2025-24-00959/7/2025
Launched in 2022, AeroSolfd, a HORIZON Europe project, aims to advance clean urban mobility by developing affordable and sustainable retrofit solutions for gasoline vehicles. This three-year initiative addresses not only tailpipe emissions but also brake emissions and pollution in semi-enclosed environments. Within AeroSolfd, the Swiss-based VERT association focuses on reducing tailpipe emissions using state-of-the-art Gasoline Particulate Filter (GPF) technology featuring an uncoated ceramic multicell wall-flow filter. VERT, in partnership with HJS, CPK, BFH, developed and tested a GPF-retrofit system at Technology Readiness Level 8 (TRL 8). Results demonstrate over 99% filtration efficiency for particles smaller than 500 nm on standard cycles (WLTC) and real-world driving cycles (RDE). Forty-two gasoline vehicles (GDI and PFI) were retrofitted with the GPF retrofit across Germany, Switzerland, Israel, and Denmark over a 6 to 8-month operational period. No issues were observed with
Rubino, Lauretta, Mayer, Andreas C., Lutz, Thomas W., Czerwinski, Jan, Larsen, Lars C.
AeroSolfd: Advancing Air Quality through Retrofitted Gasoline Particulate Filters – Insights from Laboratory and Real-World Evaluations2025-24-00889/7/2025
This article details the experimental and testing activities of the EU project AeroSolfd, with a particular focus on the project's efforts to reduce combustion-based nanoparticle emissions in exhaust gases for the European fleet of vehicles by developing a GPF retrofit solution. The technical activities undertaken the process of developing such a retrofit are examined in this article. The findings illustrate the viability of reducing nanoparticle levels in gasoline-powered vehicles with the utilization of appropriate GPFs. For this purpose, in addition to a fleet, four vehicles were examined in great detail and underwent the process of obtaining component approval for the particulate filter. The vehicles were measured in a preliminary state, then following the installation of the GPF, and subsequently after several months of continuous field operation. A total of four vehicles were selected for evaluation as a representative subgroup of a larger test fleet of vehicles in the project
Engelmann, Danilo, Mayer, Andreas, Comte, Pierre, Rubino, Lauretta, Larsen, Lars
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