Browse Topic: Maintenance, repair and overhaul (MRO)

Items (1,238)
Vehicle vibrations during precision instrument transport can cause damage and failure. Existing vibration isolators often lack reliability, mass production feasibility, and easy maintenance. In this paper, we design and analyze a quasi-zero-stiffness vehicle-mounted isolator with an inerter, decreasing dynamic stiffness while raising the effective mass. Theoretical, simulation, and experimental results show improved isolation performance, lower isolation frequency, and a broader isolation bandwidth.
Li, KaiLv, SiboSun, NingDai, Shijie
The reliability of aviation maintenance personnel directly impacts flight safety, yet systematic methodologies for the quantitative prediction of human error probability (HEP) in this domain remain lacking. To address this gap, a novel human factors reliability analysis method for aviation maintenance is proposed, extending the SPAR-H model through Evidential Reasoning (ER). This method is implemented as follows: Maintenance tasks are decomposed into subtasks. Subsequently, the eight types of Performance Shaping Factors (PSFs) for each subtask are evaluated by domain experts according to defined PSF levels. Expert judgments are then aggregated using Evidential Reasoning theory, enabling the calculation of aggregated PSF levels. These aggregated levels are interpolated to determine the corresponding impact multipliers. Finally, the HEP for aviation maintenance operations is calculated by integrating the SPAR-H basic error probability model with task series/parallel logic rules. The
Meng, MengMa, NingGuan, ZhongqingHan, ZuyangNan, WenxueCai, Hongbin
The automotive air-conditioning service ports task force conducted a field survey with MACS (Mobile Air Climate Systems Association) in June 2021. The scope of this survey was to determine the types of failures reported primarily at member service shops related to automotive air-conditioning service ports.
Interior Climate Control MAC Supplier Committee
Individuals who complete the applicable modules aligned with this training document will be able to define the type of damage, define the extent of damage, determine if further inspection is required, evaluate the damage against published allowable damage limits, and provide accurate documentation of the damage. The intended outcome of the training is increased safety such that no aircraft is released with unknown damage and that the aircraft meets continued airworthiness requirements. The goal is to change the culture from damage discovery to damage reporting while also reducing or eliminating flight delays due to incorrect or insufficient information. Teaching levels have been assigned to the curriculum to define the knowledge, skills, and abilities graduates will need. Minimum hours of instruction have been provided to ensure adequate coverage of all subject matter including lecture and practical exercise. These minimums may be exceeded and may include an increase in the total
AMS CACRC Commercial Aircraft Composite Repair Committee
This work presents the development of a 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. The proposed framework allows key parameters describing both the vehicle and its usage to be explicitly defined, 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 incorporated, enabling the same vehicle to be assessed under different geographic contexts and highlighting the strong
Gastaldi, ChiaraCibrario, Luca
Researchers from CompPair and the European Space Agency have developed a new composite material for spacecraft with an embedded healing agent. European Space Agency, Paris, France Healable spacecraft structures could soon be possible thanks to cutting-edge composite technology. Swiss companies CompPair and CSEM, and Belgian company Com&Sens have partnered with the European Space Agency (ESA) to modify their self-healing carbon fiber product for use in space transportation. Project Cassandra - an abbreviation for Composite Autonomous Sensing and Repair - includes sensors and a heating element within a composite carbon-fiber material, allowing spacecraft to autonomously repair initial stages of damage.
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
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 KPanda, Ajay KumarShaikh, Altafhusen
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, SunithaG.V.V., Ravi KumarPoovalingam, SundaresanVaderahobli, Devaraja Holla
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 ReddyPrathipati, Krishna ChaitanyaKandukuri, Ravindra
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, UjwalJain, Dilip KumarKulkarni, NinadKalaiyarasan, AravindhJha, AshishShenoy, Karthik
This specification establishes requirements for a standard contaminant that can be used to represent typical soils encountered in aerospace cleaning. This standard contaminant consists of materials that are common contaminants found in aircraft maintenance depots and manufacturing facilities.
AMS G9 Aerospace Sealing Committee
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
This SAE Aerospace Recommended Practice (ARP) describes standard methods of heat application to cure thermosetting resins for commercial aircraft composite repairs. The methods described in this document shall only be used when specified in an approved repair document or with the agreement of the Original Equipment Manufacturer (OEM) or regulatory authority.
AMS CACRC Commercial Aircraft Composite Repair Committee
This SAE Aerospace Recommended Practice (ARP) describes and gives general guidelines on use and applicability of standard methods for impregnating dry fabric and lay-up of the impregnated plies. The methods of impregnating dry fabric and ply lay-up described in this document have specific application and are not interchangeable. The methods should only be used when specified in an approved repair procedure or with the agreement of the Original Equipment Manufacturer (OEM) or regulatory authority.
AMS CACRC Commercial Aircraft Composite Repair Committee
This Aerospace Recommended Practice (ARP) describes methods of vacuum bagging, a process used to apply pressure in adhesive bonding and heat curing of thermosetting composite materials and metalbond for commercial aircraft parts. If this document is used for the vacuum bagging of other than thermosetting composite materials and metalbond, the fitness for this purpose must be determined by the user. The methods shall only be used when specified in an approved Repair Document or with the agreement of the Original Equipment Manufacturer (OEM).
AMS CACRC Commercial Aircraft Composite Repair Committee
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, YixuanXue, ShunBi, XiangWei, XingKang, RanyuJue, JieCheng, Liruiran
This document applies to off-road forestry work machines defined in SAE J1116 or ISO 6814.
MTC4, Forestry and Logging Equipment
This paper explores the potential of three different hybridization solutions for a medium-sized rotorcraft: an electric tail rotor, an "eco-mode", and a "boost-mode". The solutions were evaluated as a retrofit to a generalized medium lift rotorcraft and the impact on performance across five mission types, representative of the typical use cases for a military rotorcraft, was assessed. Two separate rotorcraft performance modelling tools were used to carry out the assessment, allowing for the results to be cross-examined. The models predicted performance gains for the eco-mode configuration when utilizing the single engine cruise capability for low-speed applications. Likewise, the models predicted improved performance for the boost-mode configuration when operating at hot and high (6,000 ft, 95°F) conditions due to the increased power provided by the battery system. However, all three solutions suffered from increased platform empty weight which negatively impacted performance at
Hopkins-Bain, AaronVegh, MichaelGoldberg, Chana
Helicopter maintenance troubleshooting faces significant challenges due to fragmented documentation, outdated procedural manuals, and reliance on human expertise, all of which threaten flight safety and operational efficiency. While Knowledge Graphs (KGs) effectively model hierarchical system relationships and causal dependencies, they struggle with dynamic unstructured data. Conversely, Retrieval-Augmented Generation (RAG) systems access technical manuals but risk hallucinating unsafe procedures without structural grounding. This paper introduces KG-RAG, a novel hybrid troubleshooting framework specifically engineered for helicopter systems, addressing a critical gap as existing work focuses predominantly on fixed-wing aircraft. The framework merges knowledge graphs modeling fault causality and maintenance history with multi-dimensional retrieval combining graph-based reasoning, vector embeddings, and keyword-based search. This integration enables contextual interpretation of
Majeti, RohinWende, GerkoRaddatz, FlorianRaju, Bhavana
This paper describes the characteristics of the Leonardo Advanced Tiltrotor Aircraft (ATA) concept, focusing on the relationship between goals, targeted improvements and enabling design features. The paper shows the design drivers such as performance, operational capabilities, and maneuverability and it describes how the attributes of the concept originated, showing trade-off and compromises approached during the genesis of the concept. The design drivers are translated into areas of interests, including download, drag, aerodynamic efficiency, rolling and yawing inertia, detectability, maintainability and engine retrofit ability. Finally, these areas are linked to the physical features of the concept, showing how they have been selected and combined to achieve the best overall benefit at platform level.
Bianco Mengotti, RiccardoViganò, LucaCassinelli, CarloSampugnaro, LucaPecoraro, MatteoLilliu, CristianMedici, Luca
Rolling-element bearings in rotorcraft dynamic systems are critical components susceptible to rolling contact fatigue (RCF), a dominant degradation mechanism manifesting through subsurface-initiated spalling, surface micropitting, and fatigue fractures. Robust inspection strategies compliant with EASA and FAA requirements are therefore essential. Traditional methods are often invasive, requiring disassembly, and are susceptible to human-factor errors. Smart Duplex introduces a design-for-monitoring architecture integrating in-situ videoscopic and coherence scanning interferometry (CSI) for high-resolution 3D surface mapping, including under partial grease coverage. This paper details a repeatability and reproducibility (R&R) framework ensuring metric consistency; a maintainability assessment projecting significant man-hour reductions and high availability; certification rationale emphasizing airworthiness improvements via enhanced detectability, workload reduction, and digitized
Delli Paoli, MicheleAnaclerio, Mario Alberto
Hybrid bearings, which pair traditional bearing-steel raceways with ceramic rolling elements, can offer improved performance over full-metal bearings, particularly in aerospace applications. Because rolling-element bearings are critical components, effective condition monitoring is essential to prevent in-flight failures and support proactive maintenance strategies. Wear-debris monitoring is widely used in these applications to detect and diagnose bearing fault modes. To compare degradation behavior and monitoring signatures, bearing life tests were conducted on hybrid and full-metal bearings under matched Hertzian stress conditions. The results showed that differences in degradation curves between the two bearing types were small relative to the overall variability in bearing life. Additionally, hybrid bearings that develop rolling-element pitting were observed to progress toward raceway spall formation. This paper was presented at ERF Forum 51 but has been updated with new findings
Mahmoud, HassanOszmian, Adam
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).
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, JianjianZhang, ZhijieWang, ZhenfengMa, GuangtaoShi, MeijuanLiu, JingZhao, BinggenLu, Yukun
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, WeiminLv, ZhilongOu, Shiqi(Shawn)
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, StevenMathur, GursaranTaylor, Dwayne
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, TheoMazzara, Bill
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, TrevonteMummert, TaigeKaiser, CalebGreer, MichaelBlack, NathanielOng, BennettTapahonso, EugeneMittal, Vikram
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
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
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 PrashanthHallberg, Linus
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, PankajDeole, KaushikHivarkar, Umesh
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.
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, MuyunDong, QiaoLin, Yelong
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
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, TapdigBozdereli, Arzu
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, YuchengZhao, KunyangChang, LingsuWang, XiangjunHan, TianhuiLi, Zonghe
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, JianjieLuo, XingyuMei, HongliangLu, YuxiangWang, ZhiyuanChen, Weiya
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, ZilongLiu, ChunyaHuang, TaoKou, YujiaoXie, BingleiXue, Xue
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.
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, TongGu, XuefeiLi, Lingxiao
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
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, BhuvaneshwarTirumala, BhaskarBadgujar, SwapnilHK, Shashikiran
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
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, TosifThakur, AnilTripathi, Ashish
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
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, KumareshVashist, Devendra
Items per page:
1 – 50 of 1238