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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.

General aviation aircraft has a wide range of applications, and effective cost management is one of the hot spots in the research of general aviation manufacturers. The purpose of this paper is to build a flexible engineering method to predict maintenance cost of general aviation aircraft.

To establish a reasonable general aviation aircraft maintenance cost prediction model, it is necessary to analyze the influencing factors and extract the main components of maintenance cost. The maintenance cost is divided by engineering method, and the estimation model of each component cost is established. Then, the general aviation aircraft maintenance cost model is obtained. The results show that the relative error of this method is between 13% and 20%, which has a good estimation accuracy and can be effectively used to estimate the maintenance cost of general aviation aircraft.

The maintenance cost plays an important role in the life cycle cost of general aviation aircraft. Accurate cost prediction method is of great significance to the optimal design of general aviation aircraft. However, there are few prediction models suitable for maintenance cost, the proposed approach is meaningful and quite desirable.

To some extent, this method overcomes the shortage of the work on maintenance cost prediction for general aviation aircraft. The model established in this paper has certain generality, which can provide some reference for general aviation aircraft design and operation enterprises.

Maintenance stands are the most valuable maintenance resources and provide the necessary maintenance space and maintenance facilities for aircraft maintenance. To expand the maintenance market, maintenance, repair and overhaul (MRO) urgently need to achieve a reasonable schedule between aircraft maintenance requirements and maintenance stand capability to improve aircraft maintenance continuity and reduce the risk of scratching due to aircraft movement. This study aims to design a maintenance stand scheduling (MSS) model based on spatiotemporal constraints to solve the problem of maintenance stand schedules.

To address the problem of maintenance stand schedules, this study introduces mixed-integer programming algorithm to design the MSS model on the basis of classical hybrid flow shop structure. When designing the optimization objective function of MSS modeling, the spatiotemporal constraints are mainly considered. Specifically, first, the spatial constraints between maintenance stands are fully considered so that more aircraft can be parked in the workshop. Second, the optimization objective is designed to minimize the number of aircraft movements by defining multiple maintenance capabilities of the stand. Finally, a solution based on spatiotemporal constraints is proposed in the solving process.

A set of MRO production data from Guangzhou is used as a test data set to demonstrate the effectiveness of the proposed MSS model.

The types of maintenance stands are defined and divided into four categories: fixed stand, temporary stand, half-body stand and engine ground test stand, which facilitates optimal modeling; a new scheduling model is designed considering both temporal constraints and spatial constraints, which can improve both the utilization of maintenance stand and safety (reduce the risk of scratching between aircraft).

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 elaborate on the existing link between maintenance practices and the digital world while also highlighting any unaddressed potential for digital transformation in aircraft maintenance. Additionally, explore how digital technologies contribute to optimizing efficiency within the continuing airworthiness management (CAM) processes.

A literature review was performed to provide a precise review of the authority regulations on CAM processes and existing literature on digital transformation, including artificial intelligence, machine learning, neural network and big data in civil aircraft maintenance and continuing airworthiness processes. This method is used to organize, analyze and structure the body of literature to identify research gaps in the selected scope of the study.

The high position of digital technologies in preventive and predictive maintenance and the need for legislative development for using them in CAM are emphasized. Moreover, it is shown in which area of CAM scientific research has been performed regarding the application of frontier digital technologies. In addition, the gaps between maintenance practices and the digital world, along with the potential scopes of digital transformation which has not been well addressed, are identified. And finally, how digital technologies can effectively increase efficiency in CAM processes is discussed.

To the best of our knowledge, no study comprehensively determined the body of existing knowledge on the aspects of digitalization related to the field of continuing airworthiness management and aircraft maintenance. The results of this study provide a positive contribution to airlines, policymakers, manufacturers and maintenance organizations achieving additional benefits from the implementation of digital technologies in the CAM processes.

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

Research questions: Is there a systemic relationship between different methods of screening candidates for predictive maintenance? How do the goals of a predictive project influence the choice of a dropout method? How do the company’s characteristics implementing the predictive project influence the selection of the dropout method?

The authors described and compiled a taxonomy of currently known methods of screening candidate aircraft components for predictive maintenance for maintenance, repairs and overhaul organizations; identified the boundaries of each way; analyzed the advantages and disadvantages of existing methods; and formulated directions for further development of methods of screening for maintenance, repairs and overhaul organizations.

The authors identified the relationship between various screening methods by developing the approach proposed by Tiddens WW and supplementing it with economic methods. The authors built them into a single hierarchical structure and linked them with the parameters of the predictive project. The principal advantage of the proposed taxonomy is a clear relationship between the structure of the screening methods and the goals of the predictive project and the characteristics of the company that implements the project.

The authors of the article proposed groups of screening methods for predictive maintenance based on economic indicators to improve the effectiveness and efficiency of the screening process.

The airlines cancel their flights frequently because of factors that they do not have any control over. Spare aircraft can potentially address some of the issues caused by cancelled flights. This paper aims to offer an exploratory study into the financial and operational viabilities of spare aircraft for airlines.

Mathematical models are proposed to evaluate the financial and operational metrics under different scenarios. The models are applied to Delta, Spirit and Southwest Airlines with different business models. All data are extracted from US Bureau of Transport Statistics, Cirium Diio Mi and CAPA databases. The IBM Cplex solver was used to execute the binary linear program models.

The research revealed that factors such as airline network size, hub and spoke structure and average weekly flight cancellations are crucial in establishing the need for spare aircraft. For the number of weekly cancellations, there exist break-even values that reasonably justify spare aircraft.

Models can be customized and applied to other modes of transportations.

This study is the first to consider the use of spare aircraft in airlines from both financial and operational perspectives within the scope of the mathematical model. The analyses identify financial break-even points for a number of spare aircraft and their home base locations for three airlines. Operational utilization of spare aircraft is studied and contrasted with financial metrics.

Industry 4.0 has been identified as a key cornerstone to modernise economies where man and machines complement each other seamlessly to achieve synergies in decision-making and productivity for contributing to SDG 8: Decent Work and Economic Growth and SDG 9: Industry, Innovation and Infrastructure. The integration of Industry 4.0 remains a challenge for the developing world, depending on their current status in the industrial revolution journey from its predecessors 1.0, 2.0 and 3.0. This chapter reviews reported findings in literature to highlight how robotics and automated systems can pave the way to implementing and applying the principles of Industry 4.0 for developing countries like Mauritius, where data collection, processing and analysis for decision-making and prediction are key components to be integrated or designed into industrial processes centred heavily on the use of artificial intelligence (AI) and machine learning techniques. Robotics has not yet found its way into the various industrial sectors in Mauritius, although it has been an important driver for Industry 4.0 across the world. The inherent barriers and transformations needed as well as the potential application scenarios are discussed.

Unmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.

Maintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.

A novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.

The proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.

Two important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.

This research is original work to determine a maintenance program for unmanned aircraft that their application has rapidly grown up. Practical and design parameters have been considered in this work.