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According to regulations, aircraft must be in an airworthy condition before they can be operated. To ensure airworthiness, they must be maintained by an approved component maintenance organisation. This study is aimed to identify potential errors that may arise during the final inspection and certification process of aircraft components, categorise them, determine their consequences and quantify the associated risks. Any removed aircraft components must be sent to an approved aircraft component maintenance organisation for further maintenance and issuance of European Union Aviation Safety Agency (EASA) Form 1. Thereafter, a final inspection and certification process must be conducted by certifying staff to receive an EASA Form 1. This process is crucial because any errors during this stage can result in the installation of unsafe components in an aircraft.

The Systematic Human Error Reduction and Prediction Approach (SHERPA) method was used to identify potential errors. This method involved a review of the procedures of three maintenance organisations, individual interviews with ten subject matter experts and a consensus group of 14 certifying staff from different maintenance organisations to achieve the desired results.

In this study, 39 potential errors were identified during the final inspection and certification process. Furthermore, analysis revealed that 48.7% of these issues were attributed to checking errors, making it the most common type of error observed.

This study pinpoints the potential errors in the final inspection and certification of aircraft components. It offers maintenance organisations a roadmap to assess procedures, implement preventive measures and reduce the likelihood of these errors.

The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights.

The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered.

As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets.

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

Blockchain is the fastest-growing technology currently being used in the aviation industry, especially in aviation maintenance, repair and overhaul (MRO) services. This study aims to create an analytic framework to assess the main factors and subfactors that have significantly influence the blockchain used in aviation MRO services. A mixed-methods approach is used to gain a comprehensive understanding of how blockchain is being adopted in aircraft maintenance facilities, Semi-structured interviews and questionnaires are used to gather data. The questionnaire is focused on the present state of the MRO industry.

Based on the literature review, a framework including four factors and 12 subfactors is developed, and the analytic hierarchy process (AHP) is then established. This study explores how these factors influence the implementation of blockchain in aviation MRO services. The five aviation MRO services providers in Taiwan, namely, “Evergreen Aviation Technologies Corporation,” “Taiwan Aircraft Maintenance and Engineering Co., Ltd.,” “Air Asia Company Ltd.,” “Aerospace Industrial Development Corp.” and “GE Evergreen Engine Services Corporation” are considered; furthermore, 55 experts working in these organizations were invited to evaluate the relative importance criteria in the AHP framework.

The results indicate that “inventory management” is the most important criterion, followed by “provisioning, procurement and sales” and “maintenance planning.” In addition, the three most important subfactors are “parts interchangeability,” “customer stock” and “SPEC2K interface for ATA SPEC 2000.”

Asia is ranked as the second most important aviation MRO service market in the world. Taiwan has the shortest flight hours in the western Pacific region, the seven major foreign cities in this area. Aviation MRO service providers located in Taiwan are the best choices for aircraft MRO in the Asia-Pacific region, indicating that Taiwan serves as a promising market development evaluation model for blockchain aviation MRO services. The results offer a comprehensive overview of the relative importance of different criteria for MRO services that use blockchains. In addition, the findings present the market potential for key players in the aviation industry, including aircraft engineers, airline companies, aircraft component manufacturers and aviation MRO service providers.

The 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.

The 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.

The 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.

The 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.

This study aims to provide an example of curriculum development for vocational higher education in aviation, specifically in the aircraft maintenance engineering program, while considering the anticipated technological changes in the industry.

Qualitative methods, including document analysis, in-depth interviews, and focus group discussions, were utilized to collect, and analyse data.

The findings demonstrate that redesigning the curriculum through course reconstruction, integrating independent learning methods, and adopting blended learning approaches holds significant potential for enhancing the education of future aircraft maintenance engineers.

These endeavours contribute to the cultivation of highly skilled graduates who are adept at navigating technological advancements and making valuable contributions to the competitiveness and safety of the aviation industry.

With the advancements in electric vertical take-off and landing (eVTOL) aircraft technology such as batteries, mechanisms, motors, configurations and so on, designers and engineers are encouraged to create unique and unconventional configurations of eVTOL aircraft to provide better capabilities and higher efficiencies to compete in the market. The box fan-in-split-wing tiltrotor eVTOL aircraft is an innovative design that aims to address the aerodynamic inefficiencies such as propeller effects in cruise and engine mounts drag that existed in traditional eVTOL aircraft designs such as vectored thrust, rotorcraft, lift + cruise and multi-copter configurations. This paper aims to propose a multi-disciplinary design process to conceptually design the box fan-in-split-wing Tiltrotor eVTOL aircraft.

An unconventional methodology was used to design the UAM aircraft, and the following parameters are considered: capable of vertical take-off and landing, highly aerodynamic with a high lift-to-drag ratio, low Cd₀ modern and appealing, rechargeable or battery swappable and feature to minimise or negate propeller drag. A heavy emphasis on improving performance and weight based on aerodynamics was enforced during the conceptual design phase. MAPLA and XFOIL were used to identify the aerodynamic properties of the aircraft.

Upon determining the key parameters and the mission requirements and objectives, a list of possible VTOL configurations was derived from theoretical and existing designs. The fan in the wing/split wing was selected, as it could stow the propellers. A tiltrotor configuration was selected because of its ability to reduce the total number of lift props/motors, reducing powerplant weight and improving aerodynamic efficiency. For the propulsion configuration, a battery–motor configuration with a hexa-rotor layout was chosen because of its ability to complement the planform of the aircraft, providing redundant motors in case of failure and because of its reliability, efficiency and lack of emissions. Coupled with the fan-in-wing / split wing concept, the box wing seamlessly combines all chosen configurations.

The box fan-in-split-wing Tiltrotor eVTOL aircraft aims to address the aerodynamic inefficiencies of earlier designs such as propeller effects in cruise and engine mounts drag. The potential benefits of this aircraft, such as increased range, endurance and payload capacity, make it an exciting prospect in the field of Urban Air Mobility.

This study aims to address the critical issue of machine breakdowns in industrial settings, which jeopardize operation economy, worker safety, productivity and environmental compliance. It explores the efficacy of a predictive maintenance program in mitigating these risks by proactively identifying and minimizing failures, thereby optimizing maintenance activities for higher efficiency.

The article implements Logical Analysis of Data (LAD) as a predictive maintenance approach on an industrial machine maintenance dataset. The aim is to (1) detect failure presence and (2) determine specific failure modes. Data resampling is applied to address asymmetrical class distribution.

LAD demonstrates its interpretability by extracting patterns facilitating the failure diagnosis. Results indicate that, in the first case study, LAD exhibits a high recall value for failure records within a balanced dataset. In the second case study involving smaller-scale datasets, enhancement across all evaluation metrics is observed when data is balanced and remains robust in the presence of imbalance, albeit with nuanced differences in between.

This research highlights the importance of transparency in predictive maintenance programs. The research shows the effectiveness of LAD in detecting failures and identifying specific failure modes from diagnostic sensor data. This maintenance strategy exhibits its distinction by offering explainable failure patterns for maintenance teams. The patterns facilitate the failure cause-effect analysis and serve as the core for failure prediction. Hence, this program has the potential to enhance machine reliability, availability and maintainability in industrial environments.

The objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.

Effective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.

Based on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.

Early air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.

To the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

This study aimed to develop an adapted collaborative governance model for aviation human resource development in Indonesia's approved training organisations (ATO), considering the expected changes in the industry due to advanced technologies. The model, based on Ansell and Gash's approach, emphasizes multi-stakeholder collaboration to ensure workforce development aligns with industry and regulatory standards and accommodates technological advancements.

Qualitative methods, such as in-depth interviews and focus group discussions, were employed to collect and analyse data.

The results indicated that collaborative governance is a valuable tool for cultivating competent human resources and facilitating industry improvement in the face of rapid technological change.

The proposed model contributes significantly to the field by promoting inclusive and effective human resource development through the Centre for Aviation Human Resource Development (CAHRD), thereby preparing the Indonesian aviation industry for the impact of advanced technologies. Furthermore, this study contributes to the enhancement of Ansell and Gash's collaborative governance theoretical framework by effectively addressing its empirical gaps concerning vocational education and training challenges within Indonesia's air transportation sector.