19 research outputs found

    Tool development for automating the structural integrity assessment of aircrafts’ components

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    During the aircraft’s maintenance, it is essential that its structural inspection as well as potential found damages to be assessed in an effective manner within a limited time frame. To reduce the required time, for the aforementioned procedure, an innovative tool is developed. For the first time, a structural integrity assessment tool integrates image processing algorithms and FE models. The tool processes data from Non Destructive Testing (NDT), via image processing algorithms, and then provide pieces of information regarding the residual strength of the examined component in an automated way. The data acquired, from the NDT, are interpreted using the threshold method, in order to generate an accurate map of the component with its damages (size and location). This information automatically feeds a parametric Finite Element (FE) model, which forms an FE model of the damaged component ready for analysis. The parametric FE model can construct models for isotropic and laminated monolithic components using the XFEM method and the Cohesive Zone Modelling (CZM) technique respectively. For the case of laminated components, Progressive Damage Modelling (PDM) is also considered. In this study, simple geometries as the upper and lower wing skin, are analysed under static tension or compression. Aim is to examine the effectiveness and feasibility of the developed tool. The main benefits obtained from the application of the tool is that the image processing data are manipulated automatically and fast providing all the information regarding the location and size of damage in the examined component. Moreover, from the obtained numerical results the strength reduction of the component can be predicted well, by just comparing them with the respective numerical results of the reference undamaged component. Overall, the developed tool is very promising, since time saving can be gained

    An experimental study on the applicability of acoustic emission for wind turbine gearbox health diagnosis

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    Condition monitoring of wind turbine gearboxes has mainly relied upon vibration, oil analysis and temperature monitoring. However, these techniques are not well suited for detecting early stage damage. Acoustic emission is gaining ground as a complementary condition monitoring technique as it offers earlier fault detection capability compared with other more established techniques. The objective of early fault detection in wind turbine gearboxes is to avoid unexpected catastrophic breakdowns, thereby reducing maintenance costs and increase safety. The aim of this investigation is to present an experimental study the impact of operational conditions (load and torque) in the acoustic emission activity generated within the wind turbine gearbox. The acoustic emission signature for a healthy wind turbine gearbox was obtained as a function of torque and power output, for the full range of operational conditions. Envelope analysis was applied to the acoustic emission signals to investigate repetitive patterns and correlate them with specific gearbox components. The analysis methodology presented herewith can be used for the reliable assessment of wind turbine gearbox subcomponents using acoustic emission.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: IntelWind Project is an FP7 project partly funded by the EC under the Research for the Benefit of SMEs programme, Grant Agreement Number 283277, coordinated and managed by Innovative Technology and Science Ltd

    Portable automated radio-frequency scanner for non-destructive testing of carbon-fibre-reinforced polymer composites

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    A portable automated scanner for non-destructive testing of carbon-fibre-reinforced polymer (CFRP) composites has been developed. Measurement head has been equipped with an array of newly developed radio-frequency (RF) inductive sensors mounted on a flexible arm, which allows the measurement of curved CFRP samples. The scanner is also equipped with vacuum sucks providing mechanical stability. RF sensors operate in a frequency range spanning from 10 up to 300 MHz, where the largest sensitivity to defects buried below the front CFRP surface is expected. Unlike to ultrasonic testing, which will be used for reference, the proposed technique does not require additional couplants. Moreover, negligible cost and high repeatability of inductive sensors allows developing large scanning arrays, thus, substantially speeding up the measurements of large surfaces. The objective will be to present the results of an extensive measurement campaign undertaken for both planar and curved large CFRP samples, pointing out major achievements and potential challenges that still have to be addressed

    Acoustic emission localization on ship hull structures using a deep learning approach

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    this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

    Acoustic emission localization on ship hull structures using a deep learning approach

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    this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

    Acoustic emission localization on ship hull structures using a deep learning approach

    No full text
    this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR
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