Machine Learning Application on Aircraft Fatigue Stress Predictions

O’Higgins, Eugene; Graham, Kyle; Daverschot, Derk; Baris, Julien

doi:10.1007/978-3-030-21503-3_81

Cited by 3 publications

(2 citation statements)

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“…The ability to add a continuous amount of data points and input variables affecting these parameters with machine learning algorithms is what differs a well-formulated algorithm from a typical one. This consequentially allows for the output data to be optimized for the assessment of structural integrity and maintenance scheduling [10]. The sources of data for the machine learning algorithms include:…”

Section: Finite Element Analysis and Constraints Used For A Dgital Si...mentioning

confidence: 99%

“…3. Fencing the data by the use of a 'deterministic method' after consulting with experimental previous data and human input and decision [10] Due to the requirement of human intervention in its initial stages of data learning, a Machine Learning Model requires a set of Artificial Neural Networks (ANN) in order to formulate decisions based on more than the initial data learned [12]. This is done via the use of neurons comprising a transfer function each, and linked together via weighted branches containing factors of multiplication linking them to the input values and bias values in order to result with an output value, as shown in Figure 4 [8].…”

Section: Finite Element Analysis and Constraints Used For A Dgital Si...mentioning

confidence: 99%

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Certification Approach for Physics Informed Machine Learning and its Application in Landing Gear Life Assessment

Mir

Perinpanayagam

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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The efficacy of fatigue life approximation methodologies for Landing Gear systems is studied and compared to the ongoing Structural Health Monitoring techniques being researched, which will forecast failures based on the system's specific life and withstanding abilities, ranging from creating a digital simulation model to applying neural network technologies, in order to simulate and approximate locations and levels of failure along the structure. Explainable Artificial Intelligence allows for the ease-of-integration of Deep Neural Network data into Predictive Maintenance, which is a procedure focused on the health of a system and its efficient upkeep via the use of sensorbased data. Test data from a flight includes a multitude of conditions and varying parameters such as the surface of the landing strip as well as the aircraft itself, requiring the use of Deep NeuralNetwork models for damage assessment and failure anticipation, where compliance to standards is a major question raised, as the EASA AI roadmap is followed, as well as the ICAO and FAA. This paper additionally discusses the challenges faced with respect to standardizing the Explainable AI methodologies and their parameters specifically for the case of Landing Gear.

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Section: Finite Element Analysis and Constraints Used For A Dgital Si...mentioning

confidence: 99%

Section: Finite Element Analysis and Constraints Used For A Dgital Si...mentioning

confidence: 99%