Ageing study in aircraft electromechanical chain: systems modeling for property evolution monitoring

Pin, S.; Belijar, Guillaume; Fetouhi, Louiza; Somer, Leroy; Steen, Cyril Van de; Albert, Larry

doi:10.1109/eic47619.2020.9158659

Cited by 9 publications

(2 citation statements)

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“…In systems with complex working conditions where engineers have very limited ability to describe system dynamics, such as aero-engines, the establishment of PBMs will be difficult [87]. There are some discrete methods that can mitigate this difficulty and help the PBMs as close to the real physical situation as possible [88], such as Finite Element Methods (FEM) [89]- [92], the boundary integral method and finite differences. However, it still cannot hide the limitations of the PBMs.…”

Section: ) Physics-based Modelmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

Aslam

Wileman

et al. 2022

IEEE Access

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Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly transformative technology with profound implications. Whether it be a product development, design optimisation, performance improvement, or predictive maintenance, digital twins are changing the ways work is undertaken in various industries with multifarious business applications. Aerospace industry, including its manufacturing base, is one such keen adopter of digital twins with an unprecedented interest in their bespoke design, development, and implementation across wider operations and critical functions. This, however, comes with some misconceptions about the digital twin technology and lack of understanding with respect to its optimal implementation. For instance, equating digital twin to an intelligent model while ignoring the essential components of data acquisition and visualisation, misleads the creators into building digital shadow or digital models, instead of the actual digital twin. This paper unfolds such intricacies of digital twin technology for the aerospace community in particular and others in general so as to remove the fallacies that affect their effective realisation for safety-critical systems. It comprises a comprehensive survey of digital twins and their constituent elements. Elaborating their characteristic stateof-the-art composition along with corresponding limitations, three dimensions of the future digital twins for aerospace sector, termed as aero-Digital Twins (aero-DTs), are proposed as an outcome of this survey. These include the interactive, standardisation, and cognitive dimensions of digital twins, which if leveraged diligently could help the aero-DT research and development community quadruple the efficiency of existing and future aerospace systems as well as their associated processes.

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Section: ) Physics-based Modelmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

Aslam

Wileman

et al. 2022

IEEE Access

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“…Two materials used in standard aeronautical components are chosen here; a polyimide (PI/FEP) and a polyetheretherketone (PEEK), to examine the proposed PDIV estimation approach. Also, it can be found in many models existing in the literature that the dielectric constant εr' and the conductivity σAC are generally defined from suppliers datasheets and their variations with temperature, frequency and electric field are not always considered [3][4][5]. In some cases, this can affect the accuracy of the electric field calculation and thus, the estimation of PDIV.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Inception Voltage: Precise and Robust Numerical Estimation Based on Extensive Dielectric Spectroscopy Measurements

Kemari

Belijar²,

Laudebat

et al. 2021

2021 IEEE Electrical Insulation Conference (EIC)

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This paper provides a robust experimentalsimulation approach in predicting the partial discharge inception voltage (PDIV) for aeronautical applications. First, two polymers used as insulators in aeronautic components have been selected in this work, a polyimide and a polyetheretherketone. To study the electrical behavior of these materials, extensive dielectric spectroscopy measurements are carried out for different electric fields, temperatures and frequencies (E, T, and f). Then, a needleplane configuration is adopted for PDIV modeling using finiteelement method (FEM). As the model is strongly dependent upon the accurate estimation of the electric field, dielectric characterizations results are integrated in simulations to improve the precision of calculations. Thereafter, the PDIV can be estimated with the aid of Townsend's theory. Finally, a comparison between measured and computed PDIV values is presented and discussed.

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