A Framework for Aircraft Maintenance Strategy including CBM

Adhikari, Partha Pratim; Buderath, Matthias

doi:10.36001/phme.2016.v3i1.1622

Cited by 3 publications

(1 citation statement)

References 10 publications

(6 reference statements)

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“…While SHM promises to streamline aircraft maintenance and improve operational efficiency, its practical application faces hurdles including the need for accurate costbenefit analysis, standardization for certification, clear requirement definitions, and decision-support tools [29][30][31]. The potential weight savings and design optimization facilitated by SHM systems suggest significant benefits, yet the actual implementation within the aircraft lifecycle is constrained by these challenges.…”

Section: Structural Health Monitoring In Aviationmentioning

confidence: 99%

Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy

Kabashkin,

Perekrestov

2024

Applied Sciences

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This paper presents an in-depth exploration of the transformative impact of integrating the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) within the domain of aviation maintenance. It articulates the transition from conventional health monitoring practices to a more advanced, comprehensive health management approach, leveraging these modern technologies. This paper emphasizes the pivotal shift from reactive maintenance strategies to proactive and predictive maintenance paradigms, facilitated by the real-time data collection capabilities of IoT devices and the analytical prowess of AI. This transition not only enhances the safety and reliability of flight operations but also optimizes maintenance procedures, thereby reducing operational costs and improving efficiency. This paper meticulously outlines the implementation challenges, including technological integration, regulatory compliance, and security concerns, while proposing a future research agenda to address these issues and further harness the potential of these technologies in revolutionizing aviation maintenance.

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Section: Structural Health Monitoring In Aviationmentioning

confidence: 99%

Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy

Kabashkin,

Perekrestov

2024

Applied Sciences

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Impact of Structural Health Monitoring on Aircraft Operating Costs by Multidisciplinary Analysis

Cusati

Corcione

Memmolo

2021

Sensors

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Structural health monitoring is recognized as a viable solution to increase aviation safety and decrease operating costs enabling a novel maintenance approach based on the actual condition of the airframe, mitigating operating costs induced by scheduled inspections. However, the net benefit is hardly demonstrated, and it is still unclear how the implementation of such an autonomic system can affect performance at aircraft level. To close this gap, this paper presents a systematic analysis where the impact of cost and weight of integrating permanently attached sensors—used for diagnostics- affect the main performance of the aircraft. Through a multidisciplinary aircraft analysis framework, the increment of aircraft operating empty weight is compared with the possible benefits in terms of direct operating costs to identify a breakeven point. Furthermore, the analysis allows to establish a design guideline for structural health monitoring systems returning a safer aircraft without any economic penalties. The results show that the operating costs are lower than those of the reference aircraft up to 4% increase in maximum take-off weight. Paper findings suggest to considering a condition monitoring strategy from the conceptual design stage, since it could maximize the impact of such innovative technology. However, it involves in a design of a brand-new aircraft instead of a modification of an existing one.

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Operational Availability Optimization Model Based on the Integration of Predictive and Scheduled Maintenance

2021

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Health monitoring technologies and data analytics are increasingly widespread in the aviation industry following the growth in the capacity and speed of abundant and accurate data generation and transmission from the aircraft systems. These advances are fueling a change process in aircraft maintenance strategy towards a more proactive, precise, and effective approach consolidated in the concepts of Integrated Vehicle Health Monitoring (IVHM) and Prognostics and Health Management (PHM). Following that, several model-based and data-driven prognostics methods for Remaining Useful Life (RUL) estimation have been developed in the pursuit of improving predictive maintenance interventions for different types of components. Recent papers showcased the significant challenges faced to achieve forecast accuracy as posed by the inherent uncertainty involved in the functional dynamics of complex systems. This work acknowledges these difficulties and tackles variability by embracing it in the methodology deployed by means of considering in its framework the confidence intervals associated with the estimates for a predefined level of confidence. Nevertheless, the ability to pinpoint times-to-failure by itself is arguably not enough to yield better operational results and improve support levels of service given that scattered standalone interventions may even cause failure occurrences and total downtime to increase. This study demonstrates the rationale behind those effects and exposes the necessity for a method for achieving a compromise to optimally accommodate the concurrent economic, reliability and maintainability goals which are, respectively, the maximization of component useful life expenditure, the minimization of the running-into-failure risk and the minimization of total downtime. Further on, the article explores the problem in detail identifying the key parameters pointed out in the literature that need to be addressed by the modelling process to ensure the soundness of the method. The text then proposes a solution consisting of an innovative analytical model that optimizes operational availability through the dynamic allocation of flight-hours to each aircraft part of a fleet based on the integration of predictive and scheduled maintenance, minimizing total downtime, while accounting for prognostics uncertainty and the associated the risk of failure and incurring in corrective maintenance. The intended outcome is the capability of providing dynamic maintenance plans specially adjusted to each tail number according to its assessed health status and a calculated prognostic that considers predetermined future flights specifically attributed to optimize the overall availability of the fleet. An illustrative case study involving multiple components with different aging parameters equipping the aircraft of a small military fleet operating from a single base was used to test the solution and produced results that corroborate the validity of the approach adopted and demonstrate the model’s value and effectiveness. The results also indicate there is significant potential to expand the study and encourage its further development to contemplate multiple-base scenarios and incorporate more detailed aspects such as tasks location within the aircraft, availability of spare parts and resources in general, out-of-phase items and its implementation together with a simulation tool to generalize its application. The main contributions of the study are twofold. It adds on the theoretical complexity by tackling systems of systems instead of the predominant single component approach, and it provides a model with an optimizing objective function to improve maintenance planning in real-life.

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A Framework for Aircraft Maintenance Strategy including CBM

Cited by 3 publications

References 10 publications

Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy

Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy

Impact of Structural Health Monitoring on Aircraft Operating Costs by Multidisciplinary Analysis

Operational Availability Optimization Model Based on the Integration of Predictive and Scheduled Maintenance

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