A cross-sectoral review of the current and potential maintenance strategies for composite structures

Lopez, Javier Contreras; Chiachío, Juan; Saleh, Ali; Chiachío, Manuel; Kolios, Athanasios

doi:10.1007/s42452-022-05063-3

Cited by 10 publications

(2 citation statements)

References 238 publications

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“…Furthermore, the magnitude of the specific damage holds a pivotal role in ascertaining the ultimate cost of repair. Hence, drawing upon the practical operational experience of the airline, the cost of repair, symbolized as C repair , can be expressed as follows [49,50]:…”

Section: Maintenance Cost Evaluationmentioning

confidence: 99%

Dynamic probability modeling-based inspection intervals optimization for civil aircraft composite structures using Bayesian updating

Li,

Zuo,

Yang

2024

Smart Mater. Struct.

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The evaluation of damage tolerance in composite materials is essential for ensuring the safety of aircraft structures. One of the most challenging aspects of applying probability modeling-based methods to evaluate damage tolerance is determining the actual damage size distributions for in-service aircraft structures. Although existing nondeterministic approaches have been used to optimize inspection intervals of composite structures, few studies have investigated the effects of updates on the actual damage size distribution and its impact on both the probability of structural failure and inspection intervals. This paper proposes a dynamic optimization method for inspection intervals of composite structures based on Bayesian updating. The damage size distribution of the composite structure is characterized by a general stochastic distribution. A Bayesian updating methodology is presented to iteratively update the actual damage size distribution whenever new data becomes available. Based on the constructed probability model, the inspection intervals of composite structures are determined under the objectives of optimal safety and economy for civil aircraft using a Monte Carlo approach. Compared to prior distribution models, the proposed method achieves higher safety for structures during a single inspection, reduces the failure probability of structures throughout their entire service life, and incurs lower maintenance costs. It also enables maintenance personnel to flexibly adjust inspection intervals while facilitating quantitative evaluation of both failure probabilities and maintenance costs associated with these intervals. These findings suggest that the proposed method holds great potential in enabling maintenance personnel to make informed decisions regarding inspection intervals for improved safety and economic performance.

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Section: Maintenance Cost Evaluationmentioning

confidence: 99%

Dynamic probability modeling-based inspection intervals optimization for civil aircraft composite structures using Bayesian updating

Li,

Zuo,

Yang

2024

Smart Mater. Struct.

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“…Complex time-dependent patterns (e.g., progressive damages in composite laminates) and uncertain events (e.g., a bird strike to an airplane) cannot be taken into account without online condition monitoring [9], which is termed structural health monitoring (SHM) for the case of structures. Thus, SHM plays an important role in the diagnostics of the structures [10].…”

Section: Introductionmentioning

confidence: 99%

Developing Health Indicators for Composite Structures Based on a Two-Stage Semi-Supervised Machine Learning Model Using Acoustic Emission Data

Moradi,

Chiachío,

Zarouchas

2023

10th ECCOMAS Thematic Conference on Smart Structures and Materials

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Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This highlights the importance of monitoring composite structures through structural health monitoring (SHM) and prognostics and health management (PHM) to ensure their safe and reliable operation. SHM provides information on the current state of the structure, while PHM predicts its future behavior and determines necessary maintenance. Health indicators (HIs) play a crucial role in both SHM and PHM, providing information on structural health and behavior, but accurate determination of these indicators can be challenging due to the complexity of material behavior and multiple sources of damage in composite structures. In the present work, a model containing a developed adaptive standardization, a dimension reduction sub-model, a time-independent sub-model, and a time-dependent sub-model is introduced to address this challenge. First, the raw data collected by the acoustic emission technique monitoring composite structures under fatigue loading is processed to provide plenty of statistical features. The extracted features are adaptively standardized according to the available data until the current time. Then, the principal component analysis algorithm is employed to reconstruct a few yet highly informative features out of those statistical features. An artificial neural network is used to regress the principal components to the HI that meets the prognostic criteria. Finally, the last sub-model takes into account the time dependency of HI values during fatigue loading. In comparison to other models, the results show superior performance.

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Development Genesis of Functional Safety on the Example of an Element of the Railways Infrastructure Subsystem

Bondarenko,

Neduzha

2024

Lecture Notes in Intelligent Transportation and Infrastructure

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A cross-sectoral review of the current and potential maintenance strategies for composite structures

Cited by 10 publications

References 238 publications

Dynamic probability modeling-based inspection intervals optimization for civil aircraft composite structures using Bayesian updating

Dynamic probability modeling-based inspection intervals optimization for civil aircraft composite structures using Bayesian updating

Developing Health Indicators for Composite Structures Based on a Two-Stage Semi-Supervised Machine Learning Model Using Acoustic Emission Data

Development Genesis of Functional Safety on the Example of an Element of the Railways Infrastructure Subsystem

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