Browse Topic: Maintenance, repair and overhaul (MRO)

Items (1,308)
In the process of replacing the rollers of the fabric cart of the tobacco storage cabinet, in order to solve the problems of low replacement efficiency and high safety risk.This article proposes a specialized lifting tool for fabric cart rollers with a self-locking and adopts the screw lifting structure, which facilitates roller maintenance operations, and conducts SolidWorks Simulation calculations and dynamic simulation methods. Jinan Cigarette Factory fine cigarettes special line leaf silk temporary storage cabinet fabric car roller replacement, for example, the results show that: the average operating personnel reduced by 50%, the replacement time from 6.7h to 1.2h, efficiency increased by 458%, This innovation significantly reduces the labor intensity of maintenance personnel and ensures a safe and reliable replacement process.
Zhang, LeiXue, YifeiZhang, GeSun, YanzhaoWang, HongbinCheng, Linfeng
Desulfurization equipment in electric power industry is in a multi-field coupled corrosion environment with high temperature, high humidity, strong acid and solid-containing slurry. The annual direct economic loss of corrosion exceeds 5 billion yuan, and the equipment replacement cycle is only 1.5-2 years. Traditional protective coatings are difficult to meet the needs. The concept of “bionic barrier-intelligent response-in-situ purification” is proposed to construct multifunctional protective coatings: The Langmuir-Blodgett technique was used to alternately assemble MXene nanosheets and polysilazane. Ti-O-Si covalent bonds enhanced the interface bonding, resulting in a coating hardness of 4H and an elongation of 200%. After 1500 hours of extreme environment test, the coating has low weight loss rate, high self-repair and antibacterial rate, and its service life is extended by 8 times. The engineering application makes the maintenance period of desulfurization tower of a 660MW unit extended from 8 months to 6 years, saving 1.2 million yuan annually, and increasing 200,000 yuan annually by recovering H ˇ SO 2. It provides a cross-scale scheme for electric power corrosion protection.
Nie, PengfeiGao, JiangyuChen, Wei
With the development of controlled nuclear fusion technology, the tokamak device, as the most promising magnetic confinement fusion reactor for advanced engineering applications, requires remote maintenance of its internal components, which has become a key factor affecting both operational efficiency and safety. As a critical component directly exposed to high-temperature plasma, the divertor target plate needs to be periodically replaced and carefully maintained to ensure stable and reliable reactor operation. However, this region is subject to extreme conditions, including high temperature, high vacuum, and intense radiation, making conventional manual maintenance infeasible. This necessitates the development of intelligent and automated teleoperation systems. To address the automated assembly and disassembly requirements of divertor target plates, this study designs an integrated target plate actuator comprising key functional units: a positioning module, a screwing module, a quick-change module, and a passive compliance structure. The actuator achieves rapid and precise alignment with target plate holes, accommodates bolts of different specifications, and exhibits excellent impact resistance. Furthermore, stiffness and mechanical analyses, supported by finite element simulations, verify the actuator’s safety and reliability under high loads and impact forces. To further enhance operational performance, a segmented disassembly and assembly control strategy based on reinforcement learning is proposed, enabling the actuator to adaptively handle torque variations and ensure precise and stable bolt operations. The results demonstrate that the proposed actuator and control strategy significantly improve the accuracy, stability, and efficiency of target plate operations under complex working conditions, providing a reliable solution for automated divertor maintenance in tokamak devices.
Zang, XizheYu, XingzuCao, Zhangbin
With the development of the power industry, 10kV switchgear (circuit breaker switches) are increasingly widely applied in power grids. When performing power-off maintenance or testing on 10kV switchgear (circuit breaker switches) at substations, maintenance personnel require transport carts to move the equipment to suitable locations for operation. Traditional transfer carts suffer from structural design flaws and significant shortcomings. These include difficulty operating in the confined spaces of switchgear cabinets, high risks associated with manual handling, low efficiency in secondary transfers, and poor adaptability across multiple workstations. These issues collectively pose safety hazards during power-off maintenance or testing. When operating in a 3m×5m high-voltage room, traditional maintenance carts achieve less than 0.5 units transported per hour, with equipment damage rates reaching 3% annually due to drops, resulting in low work efficiency. To address these challenges, a 10kV switch cart maintenance platform has been developed. This standardized equipment facilitates 10kV switch cart maintenance, supporting the intelligent upgrade of power grid operation and maintenance.
Yang, SenZhou, HanSu, HainanYu, XinHu, YutaoWang, Xisheng
Fleet heterogeneity, from manufacturing variations and diverse operating conditions, complicates reliability analysis by obscuring true failure patterns in aero-engines. This is a critical challenge in an industry as inaccurate Mean Time Between Failures (MTBF) estimates threaten safety and inflate operational costs, by forcing a choice between inefficiently conservative maintenance or the risk of in-service failures. Conventional analysis often fails by pooling all fleet data. To address this, our paper presents an analytical framework that improves predictive accuracy by filtering, rather than aggregating statistical noise. The methodology uses a Randomized Block Design (RBD) and ANOVA hypothesis test to screen a diverse dataset and isolate statistically homogeneous subgroups. This filtration identifies a core fleet with a consistent failure signature, providing a purified dataset for modeling. This refined data is then modeled using both Weibull and the Exponentiated Inverse Weibull distributions to ensure the results are robust and not model-dependent. Applying this framework to a 25-engine dataset that experienced 66 failures, we isolated a stable failure pattern, yielding a primary MTBF of 171.16 hours and a cross-validated MTBF of 176.35 hours. The close 3% convergence between these models validates our approach. By providing a dependable MTBF, this work establishes a stronger foundation for data-driven Reliability Centered Maintenance (RCM). It empowers maintenance planners to move toward evidence-based intervals, safely extending engine time-on-wing, optimizing spare parts inventory, and significantly reducing direct operational costs for airlines.
Jubaid, Mayin UddinBebe, GibsonBigyen, Musa PethuelAnik, S M Kullul MehedeeYasmin, AshrafiSahran, Mohamed Sideek Mohamed
Addressing the challenges in maintaining large hydraulic cylinders and the lack of specialized equipment, this study presents a dedicated maintenance system developed through a case study of a large hydraulic lifting cylinder. Through a comprehensive analysis of maintenance requirements, we developed a six-component maintenance system comprising a foundation base, a mounting bracket, a cylinder support frame, a piston rod bracket, a drive cylinder bracket, and hydraulic components. The paper systematically explains the structural configurations and functional specifications of each component, details the operational workflow of the maintenance system, and conducts theoretical design and strength verification for critical load-bearing brackets using principles from theoretical mechanics and structural mechanics. A static analysis module from ANSYS Workbench finite element software was employed to validate the overall structure. Results demonstrate that the key components meet operational strength requirements. This innovative maintenance system proves highly feasible and serves as a valuable reference for designing similar hydraulic cylinder systems.
Qiao, XiaodongDu, ChaoLong, Yuheng
Metallurgical cranes have a high risk of structural fatigue damage and failure under complex working conditions such as high temperature, heavy load, and strong electromagnetic interference. This article proposes a data-driven structural fatigue damage health monitoring system. This system integrates fiber Bragg grating sensing technology, rigid flexible coupling multi-body dynamics simulation, and big data analysis methods to construct a sensor optimization layout strategy based on rigid flexible coupling virtual prototype simulation, achieving real-time perception of stress states in key parts such as the mid span and end beam corners of the main beam. Develop a visualization system that integrates health monitoring, damage diagnosis, and life prediction. This system can dynamically evaluate the structural health status of metallurgical cranes and predict the remaining life of the structure based on a nonlinear cumulative damage model. On site engineering applications have shown that the monitoring and prediction visualization system can effectively improve the intelligent and safe operation and maintenance level of metallurgical cranes, providing a data foundation and possibility for their predictive maintenance.
Chen, LiZhang, XuDing, Keqin
Precisely detecting multi-stage degradation (MD) in rolling bearings is crucial for keeping equipment in good shape. Yet, health indicator (HI) crafted with current single-method strategies often can't balance degradation sensitivity and monotonicity across different operating conditions. Also, common MD detection methods struggle to spotransitional samples between degradation stages in cross-condition settings. To tackle these challenges, this paper introduces a new cross-condition MD detection approach for bearings, which relies on a health indicator matrix (HIM) and a transition sample enhanced network with multi-branch encoding (TSEN-MBE). First, a degradation-sensitive health indicator (DSHI) is constructed by integrating the least absolute shrinkage and selection operator (LASSO) algorithm — with comprehensive fault frequency energy (CFFE) as the regression target — and the grey wolf optimizer (GWO), capturing intrinsic degradation characteristics of bearings. Meanwhile, to enhance the monotonicity of unsupervised HIs, a time-weighted Wasserstein distance (TWWD) metric is proposed by incorporating temporal degradation features into the Wasserstein distance-based HI construction. The DSHI and TWWD are subsequently combined to generate the HIM. This HIM serves as the driving force for the Gath-Geva (GG) fuzzy clustering algorithm, enabling it to adaptively allocate MD labels according to varying operating conditions. Ultimately, the TSEN-MBE model is constructed, employing multi-branch Transformer encoders integrated with multi-head attention mechanisms to encode and combine heterogeneous features. A joint loss (JL) function — comprising transition sample enhancement (TSE), local maximum mean discrepancy (LMMD), and cross-entropy (CE) losses — is designed to enhance the recognition of transitional samples and improve cross-condition MD identification accuracy. Experimental results on the XJTU-SY dataset validate the effectiveness and superiority of the proposed method, showing that DSHI achieves the highest average degradation angles, TWWD obtains optimal monotonicity, and TSEN-MBE outperforms comparative methods in cross-condition recognition tasks.
Ma, JinghuaWei, LaiHu, GuangqiaoYu, Xiaoxia
In light of the significant roll/pitch experienced by traditional shipboard trestles due to wave action during the transfer of maintenance personnel from the operation and maintenance vessel to the offshore wind turbine base, an analysis of ship motion states was conducted under various sea conditions and ship manufacturing parameters. The kinematic capabilities and characteristics of the actuator were defined, and the mapping relationship between the wave compensation capability of the active wave compensation trestle and key design parameters, such as actuator power, was established. Consequently, an active wave compensation trestle executive mechanism was developed, incorporating lightweight research into its design. A prototype of the active wave compensation trestle was constructed and subjected to motion compensation testing. The results indicate that the prototype can effectively maintain stability between the ship and the offshore facility, thereby enhancing the safety of transferring personnel and improving maintenance efficiency.
Sun, TieruiZhao, PengfeiXin, RanQiu, JichengYang, XiaotaoShiyuan, E
During offshore wind power operation and maintenance activities, personnel transfer and boarding procedures involve numerous safety risks. is a highly effective solution for enhancing safety during ship transfers at sea. This paper designs a compact active motion-compensating lightweight gangway capable of compensating for multi-degree-of-freedom motions induced by sea waves, including roll, pitch, yaw, and heave. The structural design is first established, and based on this configuration, the output forces of the rotary electric cylinder, roll electric cylinder, and pitch electric cylinder are analyzed. A finite element method was employed to conduct a static analysis of the gangway under extreme loading conditions. Analysis of the first six modal orders revealed that the first natural frequency of the designed gangway is significantly higher than the wave frequency, thereby effectively preventing resonance phenomena. The forward transformation matrix of the gangway was simulated using the Denavit-Hartenberg (DH) method. Simulation results indicate that the working space of the lightweight gangway meets the preset motion range requirements, thereby validating the design’s feasibility. The designed compact passageway features simple operational control, high cost-effectiveness, minimal installation footprint, and low installation and control complexity, demonstrating high practicality.
Yu, ZhigangFu, WanliZheng, BowenWang, ZhuoqunFang, Jiwen
In order to achieve precise control of refueling volume, improve oil change efficiency, reduce oil pollution and waste, a new oil change device for the reducer of the range hood equipment is studied. We design a new oil change device that integrates oil discharge and refueling functions based on the operating characteristics of the reducer in the range hood equipment. Using the rotational speed of the power pump and the flow rate of the oil pipeline as variables, we determine the refueling flow rate using a one-dimensional quadratic formula. Based on direct control theory, we optimize the relative position parameters of each component of the device, establish a control matrix, and achieve precise control. The experimental results show that the new oil change device exhibits good performance during both one-time oil discharge and refueling processes, meeting the precise control standards for refueling volume. The design and application of a new oil change device can effectively improve the efficiency and accuracy of oil change in the reducer of the range hood equipment, and have practical application value.
He, PengtaoWei, BoLiang, ZhiyuanDeng, WeirenLiang, WenbinXing, Yuquan
This Purchasing Specification (PS), AMS3970/3, specifies the batch release and delivery requirements for carbon fiber fabric epoxy prepreg used for repair. This specification is applicable only when the carbon fiber fabric epoxy prepreg is used as part of the repair system defined in AMS3970 and AMS3970/1. This specification also defines the procedure and requirements for storage life extension of materials purchased against this specification. It is only applicable for materials that are qualified against AMS3970 (refer to PRI QPL AMS3970) and shall be carried out within the responsibility of the purchaser and under control of its Quality organization.
AMS CACRC Commercial Aircraft Composite Repair Committee
This Purchase Specification (PS), AMS3970/5, specifies the batch release and delivery requirements for the companion non-structural glass fiber fabric prepreg. This specification also defines the procedure and requirements for storage life extension of materials purchased against this specification. It is only applicable for materials which are qualified and shall be carried out within the responsibility of the purchaser and under control of its Quality organization.
AMS CACRC Commercial Aircraft Composite Repair Committee
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 proposed methodology is validated using an inspection operation case study. This study establishes a methodological framework for human factors reliability analysis in aviation maintenance, providing a theoretical foundation for developing scientifically grounded prevention and control measures to enhance aviation safety levels.
Meng, MengMa, NingGuan, ZhongqingHan, ZuyangNan, WenxueCai, Hongbin
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 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.
ICTMS 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 number of training hours and/or increases in the teaching levels. The modules are intended to be a competency-based training approach. Each curriculum is a subpart of this document. Module 1 is the Composite Awareness curriculum, independent of the application. Module 2 is the Initial Inspection and Damage Mapping curriculum. Module 3 is the Special Inspection Tools curriculum. Module 4 is the Reporting, Recording, and Assessment curriculum. NOTE: While the modules in this document are technically interrelated, each module can be trained independently; modules may be selected as applicable to an operator’s or maintenance organization’s needs. The combination of the modules represents the applicable identification and assessment process for damage to composite aircraft structures (see Figure 1). Module 1 is prerequisite for attendance to the other modules. The contents of Module 1 may also be used for composite awareness training of a broader target audience, including line mechanics.
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 dependence of use-phase emissions on local energy systems. Results are reported both as total life-cycle emissions and as a phase-resolved breakdown, improving transparency and supporting a clear interpretation of trade-offs between production, operation, maintenance, and end-of-life stages. Representative scenarios demonstrate that, under a standard European context, battery electric vehicles (BEVs) achieve a reduction of approximately 32% in yearly greenhouse gas emissions compared to a baseline Euro 5 gasoline vehicle. However, this trend reverses for low-mileage users relying on second-hand vehicles, for which emissions can increase by about 15%, emphasizing the critical role of usage patterns and ownership strategies in determining environmental benefits. The tool is designed to accommodate updated datasets, emission factors, and evolving energy scenarios, ensuring long-term applicability and enabling forward-looking analyses. Its capabilities are demonstrated across scenarios covering short- and long-term usage, multiple national contexts, and different powertrain technologies. The result is a robust and transparent assessment platform that enables users and policymakers to evaluate vehicle replacement strategies, providing quantitative insights into the interplay between technology, usage, and sustainability in mobility transitions.
Gastaldi, ChiaraCibrario, Luca
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 subsurface-capable mmWave sensing with AI-driven deviation analytics to enable detection of concealed cushion defects and foreign objects, including service tools, which are not reliably identified through conventional visual inspection alone. The system outputs structured maintenance reports identifying seat location, defect classification, and severity prioritization, thereby supporting targeted corrective action and reducing troubleshooting time. In addition to in-service aircraft applications, the framework is extendable to seat production and final assembly inspection environments. Establishing a digital baseline during manufacturing enables inline quality validation, structural compliance verification, and traceable lifecycle data creation. This unified digital inspection approach supports predictive maintenance modeling, reduces rework and downstream maintenance events, and enhances overall aircraft interior safety and reliability assurance.
Nagoal, Chandrasekhar ReddyPrathipati, Krishna ChaitanyaKandukuri, Ravindra
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 operational loads, accurately simulating realistic mission profiles. This approach addresses the critical "data scarcity" challenge in aviation. To ensure operational reliability, the framework incorporates Uncertainty Quantification (UQ) using Monte Carlo Dropout and addresses the "Sim-to-Real" gap through Transfer Learning on the NASA IMS bearing dataset. The HDT demonstrates a significant improvement in prognostic accuracy, achieving a Root Mean Square Error (RMSE) reduction of over 71% compared to baseline models. Furthermore, a cost-benefit analysis suggests a potential fleet savings of $240,000 per 100 engines by avoiding false negatives. This computationally efficient approach supports the Digital Engineering Transformation theme by providing a scalable blueprint for the virtual qualification of critical mechanical components.
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 reduction within the band considered and modal properties remained largely unchanged upto 200 Hz, indicating that the coating did not significantly alter the structural properties. The apparent enigma calls for a detailed study. A brief overview on the results and the plausible reasons are detailed in the paper. Out of the probable causes, the observed vibration reduction can be primarily attributed to the operational damping and mass addition effects of the PC10 coating. These findings highlight an effective and practical approach for mitigating acoustic-induced vibrations in aerospace structures, with direct application to launch vehicle stages and other aerospace hardware where post-fabrication solutions are critically needed.
Avirah, Nohin KPanda, Ajay KumarShaikh, Altafhusen
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 accounting logic and regulatory compliance reasoning, the approach produces audit-ready documentation that assists the asset owners in meeting regulatory standards from aviation authorities such as EASA and FAA. Enhancing traceability to LLPs enables their safe re-entry into operational service, supports the module swaps market, and optimizes part pooling strategies. The result is a digital enabler for circularity in the engine lifecycle—preserving material value and maintaining uncompromised safety and compliance in aviation.
Bhate, UjwalJain, Dilip KumarKulkarni, NinadKalaiyarasan, AravindhJha, AshishShenoy, Karthik
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 powered by a set of autonomous agents—each responsible for specific tasks like interpreting repair manuals, evaluating damage severity, or communicating with OEM portals. A key innovation of this system is its use of context engineering, which enables agents to share a unified, real-time view of the aircraft condition, document references, decisions made, and deadlines involved. This shared memory—dynamically updated using modern data stores and retrieval systems—ensures that agents and human experts operate with full situational awareness. The solution facilitates measurable improvements in reducing aircraft downtime, speeding up OEM coordination, and ensuring real-time continuous regulatory compliance. Engineers can take faster, more informed decisions with confidence, while human-in-the-loop oversight was preserved for critical steps such as compliance sign-off and final approvals. Overall, this intelligent MRO system transforms static, document-heavy processes into a dynamic, context-aware workflow. It brings together agent collaboration, regulatory alignment, and real-time information flow to solve one of the most pressing operational problems in aviation today. By improving inspection-to-repair cycles, enhancing SLA adherence, and enabling traceable, data-driven decision-making, this work lays the foundation for the next generation of digital MRO ecosystems that are efficient, safe, and scalable.
Abburu, SunithaG.V.V., Ravi KumarPoovalingam, SundaresanVaderahobli, Devaraja Holla
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.
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 component before it reaches the point where it goes outside its operational performance envelope by providing sufficient advance notice to deal with the issue. This document does not specify or address how each layer in the IVHM system produces or uses the data available for exchange.
HM-1 Integrated Vehicle Health Management 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 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 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, response speed and resource efficiency, thus offering a safe and efficient solution for intelligent operation and maintenance of mining equipment.
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
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 inspection records; and an airworthiness mapping supporting threat assessments, Airworthiness Limitations Section (ALS) entries, and usage-based maintenance credits. By embedding sensing capability and digitizing inspection records, Smart Duplex minimizes downtime, mitigates human-factor errors, and facilitates predictive maintenance, optimizing cost, enhancing performance, and ultimately improving safety.
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 addressing questions over the effect of impact energy on the degradation rate between hybrid and full-metal bearings.
Mahmoud, HassanOszmian, Adam
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 certain flight states.
Hopkins-Bain, AaronVegh, MichaelGoldberg, Chana
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
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 ambiguous fault descriptions, generation of precise diagnostics aligned with operational constraints, and dynamic adaptation to new fault patterns without retraining. By transforming fragmented maintenance knowledge into a verifiable, context-aware troubleshooting guide, the framework directly addresses aviation's persistent obstacles: data incompleteness, knowledge erosion, and slow safety-critical decisions. This work positions KG-RAG not merely as a tool but as a foundational shift toward cognitively augmented maintenance, elevating human expertise through AI that reasons like an engineer and contextualizes like a veteran technician for enhanced safety-critical decision-making in complex helicopter operations.
Majeti, RohinWende, GerkoRaddatz, FlorianRaju, Bhavana
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, reducing accidents, claims, and pollution. It also fits autonomous and smart cars, where tire monitoring is key.
Liu, JianjianZhang, ZhijieWang, ZhenfengMa, GuangtaoShi, MeijuanLiu, JingZhao, BinggenLu, Yukun
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 practical service protocols.
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 relatively fast for a large number of ECUs, though the primary benefit of utilizing this algorithm is that calculation times for key negotiation vary only slightly for a wide range of number of participants. This enables the easy planning of negotiation time based on the number of keys the vehicle requires without worrying about the number of ECUs that require each key. This approach also has advantages over key injection and direct key distribution schemes because it does not require a secure environment at any point in the process. Thus, ECBD can be implemented without a secure clean room in either the manufacturing or maintenance environments. This is especially valuable in the maintenance environment, as it enables easy compliance with right to repair laws without endangering vehicle cyber security.
Van Dam, TheoMazzara, Bill
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 extracted for end-to-end prediction. Experimental validation across 77 battery cells demonstrates that the framework achieves SOH estimation with a root mean square error (RMSE) of 1.41% and RUL prediction with an RMSE of 2.59% (78 cycles). Furthermore, a periodic cloud-side update and over-the-air deployment mechanism ensure long-term adaptability and cross-platform scalability without full local retraining. This intelligent prognostic framework directly enhances EV reliability and sustainability by providing health-informed decision support for optimal vehicle operation, maintenance scheduling, and the reuse of second-life batteries. Consequently, it serves as a vital tool for advancing resource optimization and circular economy principles within the E-mobility ecosystem.
Gao, WeiminLv, ZhilongOu, Shiqi(Shawn)
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 functional requirements and inform the conceptual design of a modular kit that integrates with anti-idle systems to enable new capabilities, thereby allowing vehicles to serve as mobile energy platforms in addition to their traditional role of providing mobility.
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
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. This in conjunction with data driven predictive allows for early fault detection on key subsystems such as batteries, electric motors, brake systems, and cooling systems. They tested the framework on some public benchmark datasets, and it’s pretty good at catching early anomalies with high accuracy and recall even better than the usual threshold-based methods. The study points out how important it is to use data from normal, healthy systems to build maintenance strategies that can scale well, adapt easily, and save costs, especially for connected vehicle fleets. Plus, the model helps explain what’s going on by identifying which telemetry signals are behind the anomalies, making it easier to take timely and practical maintenance actions. This work basically offers a new, practical way to keep vehicle health in check ahead of time, helping fleets stay up and running longer while cutting down on surprise breakdowns and expensive repairs.
Kumar, PankajDeole, KaushikHivarkar, Umesh
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 strategically mixing new and old battery packs, holding up key performance metrics. This can also lower the total cost of ownership by about a third. The framework produces a Battery Replacement Matrix, which sets a specific minimum State of Health (SoH) threshold for the remaining packs.
Nair, Sandeep R.Ravichandran, Balu PrashanthHallberg, Linus
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 complex nonlinear problems; Machine learning models, especially deep learning models, have obvious advantages in dealing with complex nonlinear problems and large-scale data, but they are expensive to compute and poor in interpretation. The paper further summarizes the application of different models and puts forward that the future research direction should pay attention to the complementary advantages of models and the development of hybrid models, to improve the accuracy and efficiency of pavement performance prediction.
Ma, MuyunDong, QiaoLin, Yelong
Items per page:
1 – 50 of 1308