Application of Operational Load Monitoring System for Fatigue Estimation of Main Landing Gear Attachment Frame of an Aircraft

Dziendzikowski, Michał; Kurnyta, Artur; Reymer, Piotr; Kurdelski, Marcin; Kłysz, Sylwester; Leski, Andrzej; Dragan, Krzysztof

doi:10.3390/ma14216564

Cited by 6 publications

(5 citation statements)

References 21 publications

(27 reference statements)

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“…1) Full-scale fatigue tests (FSFT) encompassing the whole structure physically being tested with methods, such as strain gauges mounted to localise and quantify strain (Dziendzikowski et al, 2021).…”

Section: Figurementioning

confidence: 99%

“…Instead, basing the replacement of the LG on the amount and severity of loads encountered can be accomplished by collecting data with the use of sensors, thereby allowing for the quantification and classification of the factors causing imminent fatigue failure. Currently, such an ideology is approached by a form of OLM systems, which consists of strain gauges placed on military aircraft (Hunt and Hebden, 2001;Dziendzikowski et al, 2021) wherein the strain output is transformed into digital signals that are thereby converted into stress histories, resulting in a loading sequence. Nevertheless, this method of fatigue assessment is inadequate for structural damage detection, by virtue of leaving out "a factor of two to three in fatigue life to be gained if damage could be monitored more adequately" (Staszewski and Boller, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Certification of machine learning algorithms for safe-life assessment of landing gear

Mir

Perinpanayagam

2022

Front. Astron. Space Sci.

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This paper provides information on current certification of landing gear available for use in the aerospace industry. Moving forward, machine learning is part of structural health monitoring, which is being used by the aircraft industry. The non-deterministic nature of deep learning algorithms is regarded as a hurdle for certification and verification for use in the highly-regulated aerospace industry. This paper brings forth its regulation requirements and the emergence of standardisation efforts. To be able to validate machine learning for safety critical applications such as landing gear, the safe-life fatigue assessment needs to be certified such that the remaining useful life may be accurately predicted and trusted. A coverage of future certification for the usage of machine learning in safety-critical aerospace systems is provided, taking into consideration both the risk management and explainability for different end user categories involved in the certification process. Additionally, provisional use case scenarios are demonstrated, in which risk assessments and uncertainties are incorporated for the implementation of a proposed certification approach targeting offline machine learning models and their explainable usage for predicting the remaining useful life of landing gear systems based on the safe-life method.

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Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Certification of machine learning algorithms for safe-life assessment of landing gear

Mir

Perinpanayagam

2022

Front. Astron. Space Sci.

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“…It is possible as many structures are subjected to a limited number of load types in their operating environment. For example, Dziendzikowski et al [7] identified only 8 load cases of main landing gear attachment frame of an aircraft (as there is a limited number of parameters set during touchdown). The following paper presents an idea where the data for SHM/OLM process is acquired using Digital Image Correlation (DIC).…”

Section: Introductionmentioning

confidence: 99%

New Operational Load Monitoring Approach Using Digital Image Correlation and Image Classification Networks

Mucha

Kokot

Viana

et al. 2023

J. Phys.: Conf. Ser.

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The following paper presents a novel approach that can be applied to Operational Load Monitoring and Structural Health Monitoring processes. The approach is based on artificial intelligence (AI) and digital image correlation (DIC) techniques. DIC is an optical method that allows measuring full-field structural displacements and strains. In the presented approach only a relatively small fragment of the material’s surface is monitored by DIC. The obtained partial image of strains or displacements is then processed by a carefully trained AI model, an image classification network, able to predict the state of whole structure (e.g. materials stresses, potential loss of material continuity). The assumption is that all possible load cases and states of the monitored structure can be identified and simulated, so the data obtained from simulations can then be used to train the image classification network. A numerical example is presented as proof of the presented concept. A modern lightweight aerostructure in the form of a hat-stiffened composite panel was used as monitored structure in the presented example and its Operational Load Monitoring was performed based on a relatively small fragment of normal strains map. The reference maps to train the network were simulated numerically. The prediction model estimates the Tsai-Wu failure criterion value for the whole composite material. The obtained accuracy of predictions proved the effectiveness and efficiency of the proposed approach.

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“…The microscale effects may be important when considering potential failure mechanisms in composite materials (microcracks, delamination, etc.). The presented approach of time-efficient determination of stresses in inclusions and matrix within composites could be used in the future, for example, in structural health monitoring (SHM) of modern structures [ 39 , 40 ], and especially in the variant of SHM known as Operational Load Monitoring processes (OLM, fatigue tracking) [ 41 , 42 , 43 ]. In OLM the amount and characteristics of load cycles that a structure endured in its operational environment are measured and registered.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Analysis of Composite Structures with Artificial Neural Network Support for Micromodel Stress Determination

Kuś,

Mucha,

Jiregna

2023

Materials

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Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress maps can be obtained. However, such stress results do not describe the actual behavior of the material and are often significantly different from the actual stresses in the heterogeneous microstructure. Finding high-accuracy stress results for such materials leads to time-consuming analyses in both scales. This paper focuses on the application of machine learning to multiscale analysis of structures made of composite materials, to substantially decrease the time of computations of such localization problems. The presented methodology was validated by a numerical example where a structure made of resin epoxy with randomly distributed short glass fibers was analyzed using a computational multiscale approach. Carefully prepared training data allowed artificial neural networks to learn relationships between two scales and significantly increased the efficiency of the multiscale approach.

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Application of Operational Load Monitoring System for Fatigue Estimation of Main Landing Gear Attachment Frame of an Aircraft

Cited by 6 publications

References 21 publications

Certification of machine learning algorithms for safe-life assessment of landing gear

Certification of machine learning algorithms for safe-life assessment of landing gear

New Operational Load Monitoring Approach Using Digital Image Correlation and Image Classification Networks

Multiscale Analysis of Composite Structures with Artificial Neural Network Support for Micromodel Stress Determination

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