Aircraft Maintenance Research Articles

Development of an Open-Source Robotic NDT Solution for Automated Composite Repair Testing

Abstract Non-destructive testing (NDT) plays an important role in aircraft maintenance and repair processes, ensuring the structural integrity necessary for safe operation. The paper presents the design and evaluation of an animated, low-cost robotic NDT system tailored for inspecting composite bonding agents. The system integrates commercially available components, including a three-degree-of-freedom robotic arm and a Raspberry Pi 4B, managed by custom Python software with a user-friendly graphical interface. Mechanical Impedance Analysis (MIA) and Eddy Current Testing (ET) methods were employed to assess the system’s performance on representative test specimens. Results indicate that the system delivers reliable and accurate measurements comparable to commercial tools like the MAUS V, while offering simplicity and modularity. Limitations such as scanning speed and handling of complex geometries are acknowledged, with potential solutions proposed for future enhancement. The system provides an affordable and customizable alternative for NDT automation in the aerospace industry.

Effectiveness Evaluation Method Using System of Systems Architecture Description of Aircraft Health Management in Aircraft Maintenance Program

This study proposes a system modeling method for aircraft maintenance program development that adopts condition-based maintenance using aircraft health management (AHM) based on a systems engineering approach, which considers AHM as a system of systems. The metamodel is tailored on the basis of the Unified Architecture Framework (UAF) and the NASA Systems Modeling Handbook for Systems Engineering. It is described using the modeling tool "Balus 2.0" (Levii, Inc). The applicability and effectiveness of a maintenance program adopting AHM is analyzed on the basis of the Maintenance Steering Group-3 (MSG-3), and its effectiveness is evaluated using the proposed system modeling method. The proposed method considers the uncertainty of the aircraft maintenance environment related to airline operations in addition to the uncertainty of the aircraft system. The effectiveness of the proposed system is investigated through a sample problem that considers a tire system using a pressure monitoring system as AHM based on the MSG-3 approach. Finally, the limitations of the proposed method are discussed.

Life cycle inventories for aviation: Background data, shortcomings, and improvements

In the context of growing environmental awareness and a drive towards sustainable aviation, Life Cycle Assessment (LCA) emerges as a pivotal tool for evaluating the environmental impacts of current and novel technologies. This paper focuses on Life Cycle Assessment within the aviation sector, with a specific emphasis on Life Cycle Inventories (LCIs) and databases. Recognizing a relevant data gap in existing databases regarding aircraft maintenance, our study seeks to address this limitation. A maintenance, repair and overhaul use-case is proposed as an illustrative example to enrich underrepresented data in LCIs. Our methodology considers the entire service life of aircraft, building a cumulative life cycle inventory in a cradle-to-gate approach. Geographical representativeness is ensured for maintenance activities conducted in Germany, with extrapolation applied across Europe where necessary. Our findings underscore the need to differentiate maintenance activities between aircraft components and engines, as well as the importance of considering various flight scenarios, ranging from short to long haul. This paper contributes to the advancement of LCA in aviation by providing insights into improving data accuracy and completeness. It also delves into how and why data generation is possible and what are the necessary data improvements within the topic. This paper is aimed at LCA practitioners in both research and industry, thus fostering sustainable practices in aviation.

Digital Twin Framework for Aircraft Lifecycle Management Based on Data-Driven Models

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle.

Treatment of aircraft maintenance wastewater by coupling low-temperature evaporation with Fe–C micro-electrolysis-configured ozonation

ABSTRACT A novel approach combining low-temperature evaporation with Fe–C micro-electrolytic ozone oxidation has been devised for treating substantial volumes of high-concentration organic cleaning wastewater generated during aircraft maintenance activities. The findings from experiments demonstrate impressive average removal rates for various contaminants, including chemical oxygen demand (93.1%), oil concentration (94.0%), suspended solids (98.9%), turbidity (98.8%), chroma (93.8%), total nitrogen (93.7%), and total phosphorus (99.2%). Within this integrated system, the synergistic effects between ozone oxidation, catalytic fillers, and electrochemical processes significantly enhance pollutant removal efficiency. The chemical oxygen demand reduction rates of the cleaning wastewater derived from engine maintenance, mechanical and electrical maintenance, aircraft accessory maintenance, and mixed wastewater exceed 98%. The optimal reaction times for four types of wastewater are 150, 120, 90, and 120 min, respectively. The time can be adjusted according to the different treatment objects in actual operation projects. Moreover, the average concentration efficiency achieved surpasses 92% while meeting all effluent quality standards set forth by municipal sewage networks, and it has reduced the disposal cost of the enterprise by about 80%. This technology provides enterprises with an effective way to reduce energy consumption and expenses.

Aircraft Skin Machine Learning-Based Defect Detection and Size Estimation in Visual Inspections

Aircraft maintenance is a complex process that requires a highly trained, qualified, and experienced team. The most frequent task in this process is the visual inspection of the airframe structure and engine for surface and sub-surface cracks, impact damage, corrosion, and other irregularities. Automated defect detection is a valuable tool for maintenance engineers to ensure safety and condition monitoring. The proposed approach is to process the captured feedback using various deep learning architectures to achieve the highest performance defect detections. Additionally, an algorithm is proposed to estimate the size of the detected defect. The team collaborated with TUI’s Airline Maintenance Team at Luton Airport, allowing us to fly a drone inside the hangar and use handheld cameras to collect representative data from their aircraft fleet. After a comprehensive dataset was constructed, multiple deep-learning architectures were developed and evaluated. The models were optimized for detecting various aircraft skin defects, with a focus on the challenging task of dent detection. The size estimation approach was evaluated in both controlled laboratory conditions and real-world hangar environments, providing insights into practical implementation challenges.

DEVELOPMENT OF MODELS AND ALGORITHMS FOR DECISION-MAKING IN AIRCRAFT MAINTENANCE AND REPAIR TASKS BASED ON THE USE OF INFORMATION TECHNOLOGIES

This article provides detailed information on aircraft maintenance and repair using information technology. In addition, special attention is paid to the development of models and algorithms for solving these problems.

Improving Pilot Competence Through Flying Practice Learning Using Flight Simulator at the Indonesia Civil Pilot Academy of Banyuwangi

This research assesses the competency and skill requirements for Seaplane aircraft maintenance in the pilot education program at the Indonesian Pilot Academy Banyuwangi. Utilizing qualitative methods, including in-depth interviews and direct observation, the study involves students and teachers in the pilot education program. Analysis reveals crucial competencies for students, encompassing a profound understanding of Seaplane aircraft systems, adept problem-solving during operations, and the capacity for quick and precise decision-making. Special skills, such as adaptability to complex working conditions and meticulousness in maintenance procedures, are also deemed essential. The research identifies alignment between the academy's curriculum and Seaplane aircraft maintenance needs, yet highlights specific areas requiring enhancement. The study recommends strengthening the curriculum and teaching methods to better address competency and skill needs. Implementing these recommendations is anticipated to elevate the quality of graduates, ensuring they are well-equipped to meet the challenges of Seaplane aircraft maintenance in the aviation industry.

Development of human resources in implementing aircraft maintenance

HR is the main factor that determines the life or death of an organization, both business and social organizations, HR planning is a science that has a specific object of study and is made a compulsory subject, both at the diploma, undergraduate and postgraduate levels. activities carried out on an ongoing basis to improve employee competence and organizational performance through training, education and career development programs. Human resource competency standards are the first door that must be passed by human resources who will be involved in the world of aviation to evaluate and analyze in the development of human resources in the field of aircraft maintenance. The method in this study uses a qualitative approach using SWOT analysis which identifies opportunities and threats that exist in the macro environment and industry as well as the company's internal strengths and weaknesses. SWOT analysis stands for (Strengths, Weaknesses, Opportunities, Threats). In this study to consider internal factors and external factors that can enhance or hinder the function of human resources in a company. Analysis that can be done by a company to accelerate the company's transition from reactive to proactive mode to improve HR strategy and function. From the test results obtained Streghts, namely the company has a Training Program for mechanics and engineers, Weaknes, namely facilities are still incomplete, mechanics are not thorough in carrying out aircraft maintenance, Opportunities have good relationships with companies, companies that have mechanics and engineers who are still young have the opportunity to add new knowledge. Threats are that larger companies offer better facilities and guarantees, companies find it difficult to find employees who have the skills that the company needs.

Modelling of Human Factors in Aviation Maintenance Using HFACS ME Human Factors Analysis and Classification System Maintenance Extension and Bayesian Network

Aircraft maintenance is a complex task involving a skilled human workforce, spare parts, and various other resources. Human factors are an inherent element of the human workforce. Human factors analysis, therefore, becomes an essential aspect of aviation maintenance. Human factors have been identified and classified using various methods in existing literature. However, there is a gap in the study of the interdependency of critical human factors including subfactors, and measuring them effectively to reduce incidents and accidents. This research work proposed a novel approach for human factors modeling using human factors analysis and classification system maintenance extension (HFACS-ME), and bayesian network (BN). Inadequate maintenance processes, inadequate documentation, inadequate supervision, Judgement decision, and attention memory were identified as some of the critical human factors in aircraft maintenance. These critical human factors were further analysed and divided into subfactors. The main contribution of the present research work is the methodology of developing a dependency model of the human factors and subfactors to analyse their measured effects on aircraft maintenance. The proposed BN model demonstrated the estimation of the probability of effective maintenance by considering the critical human factors with available facilities, and resources in an aviation maintenance setup.

Sleep Behavior in Royal Australian Navy Shift Workers by Shift and Exposure to the SleepTank App.

Rotating shiftwork schedules are known to disrupt sleep in a manner that can negatively impact safety. Consumer sleep technologies (CSTs) may be a useful tool for sleep tracking, but the standard feedback provided by CSTs may not be salient to shift-working populations. SleepTank is an app that uses the total sleep time data scored by a CST to compute a percentage that equates hours of sleep to the fuel in a car and warns the user to sleep when the "tank" is low. Royal Australian Navy aircraft maintenance workers operating on a novel rotational shift schedule were given Fitbit Versa 2s to assess sleep timing, duration, and efficiency across a 10-week period. Half of the participants had access to just the Fitbit app while the other half had access to Fitbit and the SleepTank app. The goal of this study was to evaluate differences in sleep behavior between shifts using an off-the-shelf CST and to investigate the potential of the SleepTank app to increase sleep duration during the 10-week rotational shift work schedule. Royal Australian Navy volunteers agreed to wear a Fitbit Versa 2 with the SleepTank app (SleepTank condition), or without the SleepTank app (Controls), for up to 10 weeks from May to July 2023 during the trial of a novel shift rotation schedule. Participants from across 6 units worked a combination of early (6:00 AM to 2:00 PM), day (7:30 AM to 4:30 PM), late (4:00 PM to 12:00 AM), and night shifts (12:00 AM to 6:00 AM) or stable day shifts (6:00 AM to 4:00 PM). Differences in sleep behavior (time in bed, total sleep time, bedtime, wake time, sleep efficiency [SE]) between conditions and shift types were tested using Analysis of Variance. This study was approved by the Australian Departments of Defence and Veterans' Affairs Human Research Ethics Committee. Thirty-four participants completed the full study (n = 17 Controls; n = 17 SleepTank). There was a significant effect of shift type on 24-hour time in bed (TIB24; F(4,9) = 8.15, P < .001, η2 = 0.15) and total sleep time (TST24; F(4,9) = 8.54, P < .001, η2 = 0.18); both were shorter in early shifts and night shifts compared to other shift types. TIB24 and TST24 were not significantly different between conditions, but there was a trend for greater SE in the SleepTank condition relative to Controls (F(1,9) = 2.99, P = .08, η2 = 0.11). Sleep data collected by Fitbit Versa 2s indicated shorter sleep duration (TIB24, TST24) for Royal Australian Navy workers during early and late shifts relative to stable day shifts. Access to the SleepTank app did not greatly influence measures of sleep duration but may be protective against fatigue by affecting SE. Further research is needed to evaluate the utility of the SleepTank app as a means of improving sleep hygiene in real-world, shift-working environments.

Artificial Intelligence Application on Aircraft Maintenance: A Systematic Literature Review

Artificial Intelligence Application on Aircraft Maintenance: A Systematic Literature Review

Human fatigue in the aircraft maintenance environment

The complexity of technologies in modern aircraft makes their maintenance a critical function. The work environment is diverse and dynamic, with uninterrupted working hours and multiple demands, which can result in inadequate sleep and excessive workload for Aircraft Maintenance Technicians. This causes fatigue and increases the likelihood of human errors, directly affecting safety. A study was conducted with 312 participants to investigate how fatigue affects maintenance professionals. Participants responded to questionnaires that included four culturally validated scales to measure levels of fatigue, sleepiness, workload, and quality of life. Statistical analyses were performed to correlate sociodemographic factors and work-related aspects with levels of fatigue. The results revealed a positive and significant correlation between fatigue, sleepiness, and workload, and a significant negative correlation with quality of life. It was found that 52.9% of participants experienced some level of fatigue, highlighting the prevalence of fatigue among the sampled population. Statistically significant differences were also found in participants aged 36 to 50, those working shifts, and those working during nighttime hours. Therefore, regulations and practical measures are needed to assess and manage fatigue-related risks, tailored to the aircraft maintenance environment, given that current regulations on human fatigue in aviation primarily focus on flight crew, paving the way for potential changes in the European Aviation Safety Agency regulations.

Perancangan Automatic Tail Dock Berbasis Arduino Nano Pada Pesawat Menggunakan Metode PID

The aviation industry is one of the service sectors. Due to its large number of users, safety is the primary concern in this industry. To ensure this safety, routine maintenance of operating aircraft is crucial, leading to the emergence of the aircraft maintenance industry. Due to the safety factors of the workpiece, installing ladders requires a significant workforce to supervise the workpiece, namely the aircraft itself, to prevent the ladder from hitting the aircraft body, which can result in COPQ (Cost Of Poor Quality). However, the use of many workers for ladder installation is considered inefficient because the maintenance process could be expedited if these workers could be utilized to install ladders on other parts of the aircraft. Therefore, if maintenance can be completed more quickly, the aircraft can return to flight sooner, meeting the airline's flight demands. Addressing this issue, this study aims to facilitate the ladder installation and removal process, thereby reducing the potential for collisions and ensuring the ladder is positioned at the required distance. This is achieved through ultrasonic sensors mounted on the work ladder, connected to a motor via Arduino Uno. The motor will stop when the input from the ultrasonic sensor reaches the predetermined distance. Additionally, if the aircraft tail moves while the ladder is installed, the system will automatically adjust the ladder's position to maintain the necessary distance, thus minimizing losses during aircraft maintenance.

Research on Irregular Flight Recovery Strategy Under Different Flight Route Types With Big Data Computing

During periods of extreme weather conditions, airport closures, or other unforeseen circumstances, air-lines frequently encounter disruptions in their flight schedules, posing flight recovery as a recurrent operational challenge. This paper aims to explore the impact of passenger transfer costs on flight recovery across various route types. Drawing on established methods used to calculate aircraft maintenance and crew assignment costs, this study utilizes an improved large-scale neighborhood search algorithm to perform a large number of calculations on a realistic dataset of airlines, meticulously analyzes the amalgamation of passenger transfer costs across different route types and proposes tailored strategies to mitigate disruptions. Through simulation experiments, the research evaluates the influence of various passenger transfer methods on flight recovery across diverse route types, with the goal of furnishing airlines with more adaptable and targeted decision support.

КОМПЛЕКС ТАКТИЧЕСКИХ МЕРОПРИЯТИЙ ПО СОВЕРШЕНСТВОВАНИЮ ПРОЦЕССА ИМПОРТОЗАМЕЩЕНИЯ В ОБЛАСТИ АВИАСТРОЕНИЯ В РОССИИ

This article examines the process of import substitution in the field of aircraft construction in Russia. Object: The purpose of this study is to analyze the current situation in the market of air transport services for the production of domestic aircraft and to develop tactical measures to improve the process of import substitution in the field of aircraft construction in Russia. Findings: Based on the analysis, it should be noted that the current import substitution strategy, which is currently operating in Russia, shows a serious dependence in the aircraft industry sector. Consequently, there are serious problems in the formation of domestic corporations and the creation of competitive products. Russia in the aviation industry sector shows a high proportion of import dependence and the existing serious problems of aircraft maintenance, the solution of which is relevant and should be reflected in the transformed strategy in the field of aircraft construction in Russia. Conclusions: Thus, summing up the results of the conducted research on the issue of import substitution in the field of aircraft engineering, it is worth noting that the proposed set of tactical measures and assigned tasks between the main participants in the import substitution process will create conditions for its successful implementation. Therefore, targeted financing and state support for projects to develop the country's production potential, the implementation of measures to create research laboratories based on universities and research centers, training and support of specialists, motivation of young scientists, as well as the creation and modernization of engineering centers, whose tasks will be the development and implementation of modern technologies not only in the aircraft industry, but in and into Russia's largest industrial sector, as well as in a number of other industries, it will allow improving the import substitution process in Russia. Thus, the proposed activities.

The influence of night shift work and associated factors on serum uric acid in aircraft maintenance workers

Background and objectiveThe prevalence of 12-hour shift work is increasing in various occupations. Shift work has been linked to circadian rhythm disruption, which may lead to hormonal changes and metabolic disorders, including alterations in glucose, lipid, and purine metabolism. Despite this, there is limited research on the potential connection between work shifts and abnormal serum uric acid (SUA) levels. Furthermore, the factors that contribute to abnormal SUA levels in shift workers are not well-understood. Therefore, this study aimed to analyze the SUA levels of shift workers employed in an aircraft maintenance company, investigate the potential association between shift work and SUA levels, and explore the factors that may influence abnormal SUA levels in shift workers.MethodsA total of 2263 male workers from an aircraft maintenance company were included in this study using the cluster sampling method. The workers were divided into two groups based on their working shifts: night shift (N = 1047, 46.27%) and day working (N = 1216, 53.73%). A survey was conducted between April 1st and June 30th, 2022 to gather information on work, lifestyle, physical examination results, and other relevant factors. The survey included a self-designed demographic information questionnaire to collect data on workers’ characteristics, medical history, years of employment, smoking and drinking habits, and main lifestyle behaviors. The workers’ SUA levels were measured using uricase colorimetry. One-way ANOVA was used to compare the difference in the abnormal detection rate of SUA between the two groups, and multi-factor logistic regression analysis was used to identify the factors that influence abnormal SUA levels.ResultsThe study indicated that 48.9% of night shift workers and 43.8% in the regular day workers had abnormal SUA levels, with a significant difference between the two groups (χ2 = 6.125, P = 0.013). Factors such as circadian rhythm type, shift work, age, the taste of diet, type of diet, smoking, overweight or obesity based on body mass index (BMI), concentration of urine creatinine (CREA), total cholesterol, triglyceride, and low-density lipoprotein cholesterol were found to be correlated with SUA abnormalities (P < 0.05). The risk of developing SUA abnormalities was found to be higher in individuals with an intermittent (OR = 1.34, 95% CI: 0.83–2.12, P < 0.05) or evening circadian rhythm type (OR = 1.45, 95% CI: 0.86–2.43, P > 0.05) compared to those with a morning type. Additionally, factors such as night shift work, a high-sodium diet, smoking, a diet high in meat and low in vegetables, being overweight or obese, and higher levels of CREA were also found to increase the risk of developing SUA abnormalities. The study also revealed a significant dose-response relationship between BMI and abnormal uric acid levels. After controlling for other factors, the risk of developing SUA abnormalities was found to be 1.18 times higher in the night shift work group than in the day work group (OR = 1.18, 95% CI:1.02–1.34, P = 0.01).ConclusionShift work has been linked to a higher risk of developing SUA abnormalities, and there are several factors that may contribute to this risk. To prevent diseases, it is recommended that enterprises implement better health monitoring and management practices for shift workers.

Satisfying the needs for highly qualified engineering staff in a disruptively changing aircraft industry with special care for MRO sector

PurposeThe number of passengers in the aviation sector following COVID-19 has recovered in 2023 and is 5% higher than it was in 2019. The average annual growth of air travel is predicted to be 3.2% between 2019 and 2039. This means the need for aircraft maintenance, repair and overhaul (MRO) services will also increase. Moreover, the stakeholders require lower costs, higher effectiveness/market share and sustainability. These expectations can be realized only with the identification, development and implementation of new innovations while improving and optimizing the already used processes and procedures. Since only highly qualified graduates can reach these requirements, the need for profession-specific MSc and PhD level engineers has elevated significantly. The purpose of this paper is to introduce the development and implementation of a new MRO higher educational framework program in strong cooperation with enterprises and universities.Design/methodology/approachThe emphasis is placed on the program’s scouting, investigation, development, realization and evaluation by defining key performance indexes and aiming for the optimal solution for all participants.FindingsThe result of this study is the establishment of a new educational framework, the reinvention of the MSc curriculum and the integration of PhD-level researchers in the industry in a way that fulfills the needs and requirements of the MRO sector. In return, it will give various benefits to all parties involved.Originality/valueThe novelty of this work comes from creating a new educational MSc and PhD level framework that can push the MRO industry forward and fill the gap of missing engineers in this field. Plus, the newly developed program is highly flexible and can be used by other players in the economy after making some adaptions and modifications.

FTnet: An integrated network for fusing multi-modal NDE data of lightning damage in aircraft composite materials

Lightning strikes pose a significant challenge for aircraft and wind turbine blades with Carbon Fiber Reinforced Polymer (CFRP) structures, requiring reliable damage detection techniques. Non-destructive evaluation (NDE) methods, including X-ray and Ultrasonic Testing, are effective in identifying material damage in aircraft. However, X-ray requires access to both sides of the structure, and UT requires a coupling medium between the transducer and the structure, as well as a relatively smooth surface, making both methods less feasible for routine aircraft maintenance. Other NDE techniques, such as eddy current testing and infrared thermography, can detect damage on the side struck by lightning but lack the precision needed for a comprehensive assessment. To address these challenges, this paper introduces a two-stage Fusion-Translation network (FTnet), which integrates NDE 4.0 innovations, including data fusion and advanced imaging algorithms, to optimize the NDE process. By integrating infrared and eddy current data, FTnet characterizes lightning-induced damage with enhanced depth and contour detail, demonstrating superior performance over existing methods in both qualitative and quantitative evaluations. The implementation of FTnet marks an advancement in NDE 4.0, potentially enhancing aircraft safety and streamline maintenance protocols by providing a more reliable and comprehensive assessment of lightning strike damage.

Social Sustainability Practices and Performance of Approved Aircraft Maintenance Organizations in Kenya

Social Sustainability Practices and Performance of Approved Aircraft Maintenance Organizations in Kenya