Q2765350 (Q2765350): Difference between revisions
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(Created claim: summary (P836): The object of the project concerns the development and confirmation of a methodology and an original structural integrity monitoring system (MSM) of population nominally similar aero constructions from composite materials, installed in aircraft/unmanned vehicle flocks, based on machine learning and stochastic oscillation signals, under uncertainty due to changing environmental and operational conditions (MSS), achieving: High diagnostic performa...) |
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The object of the project concerns the development and confirmation of a methodology and an original structural integrity monitoring system (MSM) of population nominally similar aero constructions from composite materials, installed in aircraft/unmanned vehicle flocks, based on machine learning and stochastic oscillation signals, under uncertainty due to changing environmental and operational conditions (MSS), achieving: High diagnostic performance, (b) robustness in changing PLS, (c) automated operation (minimum user intervention), (d) simplicity of equipment with the smallest possible number of sensors, and (e) training and operation with a small number of naturally available oscillation signals (without artificial stimulation) for possible in-flight application. The monitoring of structural integrity of aero constructions from composite materials is important for their safety and predictive maintenance, because they are subject to delamination, impact damage and other damage. Diagnostic systems based on naturally available oscillation signals are crucial for possible use on in-flight aircraft, offering simplicity, reliability and low cost. Since nominally similar aerial constructions are used in aircraft flocks, manufacturing population diagnosis is important for asset management facilitating the installation and operation of a diagnostic system, without the need for time-consuming adjustment procedures in each cluster construction. Achieving this under the above (a-e) requirements is expected to change the situation in industry. The main difficulty that needs to be overcome for efficient diagnosis in construction population is the fact that nominally similar aerial structures of composite materials never have absolutely similar properties due to variations in their construction, materials, and border conditions in the aircraft of the cluster. These lead to significant uncertainties among members of the population and jeopardise the correct diagnosis. The situation worsens when there is a combination, with also significant uncertainty caused by changing MPAs, a significant technological barrier in monitoring the structural integrity of individual structures. The beginning of this difficulty is found in the basic principle of operation of diagnostic methods based on oscillation measurements, where a failure is identified by changes it creates in the dynamics of construction, and which are recognised by the faint effects they cause on oscillation signals via advanced algorithms and microprocessors. The problem is that changes in MPAs can cause such changes in dynamics that they sometimes overlap, almost completely, changes due to faults and the diagnostic system does not achieve a reliable diagnosis. In the proposed project, which seeks to monitor structural integrity in construction population, the problem is wider and significantly more difficult. The project aims to develop a structural integrity monitoring method for population of nominally identical composite materials under uncertainty due to changing PPAs with individual objectives:1.Development of an EIA method based on machine learning by meeting the requirements: (a)-(e) described above.2.Investigation, through numerical and laboratory experiments, of the advantages of using two different kinds of measurement sensors: Light-type accelerometers and Fiber Bragg Grating (FBG). Investigation of information merging for maximum diagnostic performance.3.Confirming and evaluating the developing diagnostic method with laboratory experiments under changing PLS.4.Development of a prototype diagnostic system.5.Confirm and experimental evaluation of the performance of the prototype according to requirements (a)-(e). (English) | |||||||||||||||
| Property / summary: The object of the project concerns the development and confirmation of a methodology and an original structural integrity monitoring system (MSM) of population nominally similar aero constructions from composite materials, installed in aircraft/unmanned vehicle flocks, based on machine learning and stochastic oscillation signals, under uncertainty due to changing environmental and operational conditions (MSS), achieving: High diagnostic performance, (b) robustness in changing PLS, (c) automated operation (minimum user intervention), (d) simplicity of equipment with the smallest possible number of sensors, and (e) training and operation with a small number of naturally available oscillation signals (without artificial stimulation) for possible in-flight application. The monitoring of structural integrity of aero constructions from composite materials is important for their safety and predictive maintenance, because they are subject to delamination, impact damage and other damage. Diagnostic systems based on naturally available oscillation signals are crucial for possible use on in-flight aircraft, offering simplicity, reliability and low cost. Since nominally similar aerial constructions are used in aircraft flocks, manufacturing population diagnosis is important for asset management facilitating the installation and operation of a diagnostic system, without the need for time-consuming adjustment procedures in each cluster construction. Achieving this under the above (a-e) requirements is expected to change the situation in industry. The main difficulty that needs to be overcome for efficient diagnosis in construction population is the fact that nominally similar aerial structures of composite materials never have absolutely similar properties due to variations in their construction, materials, and border conditions in the aircraft of the cluster. These lead to significant uncertainties among members of the population and jeopardise the correct diagnosis. The situation worsens when there is a combination, with also significant uncertainty caused by changing MPAs, a significant technological barrier in monitoring the structural integrity of individual structures. The beginning of this difficulty is found in the basic principle of operation of diagnostic methods based on oscillation measurements, where a failure is identified by changes it creates in the dynamics of construction, and which are recognised by the faint effects they cause on oscillation signals via advanced algorithms and microprocessors. The problem is that changes in MPAs can cause such changes in dynamics that they sometimes overlap, almost completely, changes due to faults and the diagnostic system does not achieve a reliable diagnosis. In the proposed project, which seeks to monitor structural integrity in construction population, the problem is wider and significantly more difficult. The project aims to develop a structural integrity monitoring method for population of nominally identical composite materials under uncertainty due to changing PPAs with individual objectives:1.Development of an EIA method based on machine learning by meeting the requirements: (a)-(e) described above.2.Investigation, through numerical and laboratory experiments, of the advantages of using two different kinds of measurement sensors: Light-type accelerometers and Fiber Bragg Grating (FBG). Investigation of information merging for maximum diagnostic performance.3.Confirming and evaluating the developing diagnostic method with laboratory experiments under changing PLS.4.Development of a prototype diagnostic system.5.Confirm and experimental evaluation of the performance of the prototype according to requirements (a)-(e). (English) / rank | |||||||||||||||
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| Property / summary: The object of the project concerns the development and confirmation of a methodology and an original structural integrity monitoring system (MSM) of population nominally similar aero constructions from composite materials, installed in aircraft/unmanned vehicle flocks, based on machine learning and stochastic oscillation signals, under uncertainty due to changing environmental and operational conditions (MSS), achieving: High diagnostic performance, (b) robustness in changing PLS, (c) automated operation (minimum user intervention), (d) simplicity of equipment with the smallest possible number of sensors, and (e) training and operation with a small number of naturally available oscillation signals (without artificial stimulation) for possible in-flight application. The monitoring of structural integrity of aero constructions from composite materials is important for their safety and predictive maintenance, because they are subject to delamination, impact damage and other damage. Diagnostic systems based on naturally available oscillation signals are crucial for possible use on in-flight aircraft, offering simplicity, reliability and low cost. Since nominally similar aerial constructions are used in aircraft flocks, manufacturing population diagnosis is important for asset management facilitating the installation and operation of a diagnostic system, without the need for time-consuming adjustment procedures in each cluster construction. Achieving this under the above (a-e) requirements is expected to change the situation in industry. The main difficulty that needs to be overcome for efficient diagnosis in construction population is the fact that nominally similar aerial structures of composite materials never have absolutely similar properties due to variations in their construction, materials, and border conditions in the aircraft of the cluster. These lead to significant uncertainties among members of the population and jeopardise the correct diagnosis. The situation worsens when there is a combination, with also significant uncertainty caused by changing MPAs, a significant technological barrier in monitoring the structural integrity of individual structures. The beginning of this difficulty is found in the basic principle of operation of diagnostic methods based on oscillation measurements, where a failure is identified by changes it creates in the dynamics of construction, and which are recognised by the faint effects they cause on oscillation signals via advanced algorithms and microprocessors. The problem is that changes in MPAs can cause such changes in dynamics that they sometimes overlap, almost completely, changes due to faults and the diagnostic system does not achieve a reliable diagnosis. In the proposed project, which seeks to monitor structural integrity in construction population, the problem is wider and significantly more difficult. The project aims to develop a structural integrity monitoring method for population of nominally identical composite materials under uncertainty due to changing PPAs with individual objectives:1.Development of an EIA method based on machine learning by meeting the requirements: (a)-(e) described above.2.Investigation, through numerical and laboratory experiments, of the advantages of using two different kinds of measurement sensors: Light-type accelerometers and Fiber Bragg Grating (FBG). Investigation of information merging for maximum diagnostic performance.3.Confirming and evaluating the developing diagnostic method with laboratory experiments under changing PLS.4.Development of a prototype diagnostic system.5.Confirm and experimental evaluation of the performance of the prototype according to requirements (a)-(e). (English) / qualifier | |||||||||||||||
point in time: 3 July 2021
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Revision as of 01:02, 3 July 2021
Project Q2765350 in Greece
| Language | Label | Description | Also known as |
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| English | No label defined |
Project Q2765350 in Greece |
Statements
360,141.0 Euro
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80.0 percent
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9 November 2020
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8 August 2023
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ΠΡΙΣΜΑ ΗΛΕΚΤΡΟΝΙΚΑ ΑΒΕΕ
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38°41'1.00"N, 21°24'37.51"E
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41°10'1.60"N, 25°1'29.57"E
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41°10'1.60"N, 25°1'29.57"E
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38°41'1.00"N, 21°24'37.51"E
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Το αντικείμενο του έργου αφορά την ανάπτυξη και την επιβεβαίωση μεθοδολογίας και πρωτότυπου συστήματος παρακολούθησης της δομικής ακεραιότητας (ΠΔΑ) πληθυσμού ονομαστικά όμοιων αεροκατασκευών από σύνθετα υλικά, που τοποθετούνται σε σμήνη αεροσκαφών/μη επανδρωμένων αεροχημάτων, βάσει μηχανικής μάθησης και στοχαστικών σημάτων ταλάντωσης, υπό αβεβαιότητα λόγω μεταβαλλόμενων περιβαλλοντικών και λειτουργικών συνθηκών (ΠΛΣ), επιτυγχάνοντας: (α) υψηλή διαγνωστική απόδοση, (β) ευρωστία σε μεταβαλλόμενες ΠΛΣ, (γ) αυτοματοποιημένη λειτουργία (ελάχιστη παρέμβαση χρήστη), (δ) απλότητα εξοπλισμού με το μικρότερο δυνατό αριθμό αισθητηρίων, και (ε) εκπαίδευση και λειτουργία με μικρό αριθμό φυσικά διαθέσιμων σημάτων ταλάντωσης (χωρίς τεχνητή διέγερση) για ενδεχόμενη εφαρμογή εν πτήση. Η παρακολούθηση της δομικής ακεραιότητας αεροκατασκευών από σύνθετα υλικά είναι σημαντική για την ασφάλεια και την προβλεπτική συντήρηση τους, διότι υπόκεινται σε αποκολλήσεις (delamination), σε ζημιές από κρούση και άλλες βλάβες. Διαγνωστικά συστήματα που βασίζονται σε φυσικά διαθέσιμα σήματα ταλαντώσεων είναι καθοριστικής σημασίας για ενδεχόμενη χρήση σε αεροσκάφη εν πτήση, προσφέροντας απλότητα, αξιοπιστία και χαμηλό κόστος. Καθότι ονομαστικά όμοιες αεροκατασκευές χρησιμοποιούνται σε σμήνη αεροσκαφών, η διάγνωση σε πληθυσμό κατασκευών είναι σημαντική για το asset management διευκολύνοντας την εγκατάσταση και λειτουργία διαγνωστικού συστήματος, χωρίς την ανάγκη χρονοβόρων διαδικασιών ρύθμισης σε κάθε κατασκευή του σμήνους. Η επίτευξη αυτού υπό τις παραπάνω (α-ε) απαιτήσεις, αναμένεται να αλλάξει τα δεδομένα στη βιομηχανία. Η κύρια δυσκολία που πρέπει να ξεπεραστεί για την αποτελεσματική διάγνωση σε πληθυσμό κατασκευών, είναι το γεγονός ότι ονομαστικά όμοιες αεροκατασκευές από σύνθετα υλικά δεν έχουν ποτέ απόλυτα όμοιες ιδιότητες λόγω των διαφοροποιήσεων στην κατασκευή τους, στα υλικά και στις συνοριακές συνθήκες στα αεροσκάφη του σμήνους. Αυτά οδηγούν σε σημαντικές αβεβαιότητες μεταξύ μελών του πληθυσμού και διακινδυνεύουν τη σωστή διάγνωση. Η κατάσταση επιδεινώνεται περισσότερο όταν υπάρχει συνδυασμός, με την επίσης σημαντική, αβεβαιότητα που προκαλείται από μεταβαλλόμενες ΠΛΣ, σημαντική επί του παρόντος τεχνολογική δυσκολία (technology barrier) στην παρακολούθηση της δομικής ακεραιότητας μεμονωμένων κατασκευών. Η απαρχή αυτής της δυσκολίας εντοπίζεται στη βασική αρχή λειτουργίας των διαγνωστικών μεθόδων που βασίζονται σε μετρήσεις ταλαντώσεων, όπου μια βλάβη αναγνωρίζεται από αλλαγές που δημιουργεί στη δυναμική της κατασκευής, και οι οποίες αναγνωρίζονται από τις αμυδρές επιδράσεις που προκαλούν στα σήματα ταλαντώσεων μέσω προηγμένων αλγορίθμων και μικροεπεξεργαστών. Το πρόβλημα είναι ότι αλλαγές στις ΠΛΣ μπορεί να προκαλέσουν τέτοιες αλλαγές στη δυναμική ώστε μερικές φορές να επικαλύπτουν, σχεδόν ολοκληρωτικά, τις αλλαγές λόγω βλαβών και το διαγνωστικό σύστημα να μην επιτυγχάνει αξιόπιστη διάγνωση. Στο προτεινόμενο έργο όπου επιδιώκεται η παρακολούθηση της δομικής ακεραιότητας σε πληθυσμό κατασκευών, το πρόβλημα είναι ευρύτερο και σημαντικά δυσκολότερο. Το έργο στοχεύει στην ανάπτυξη μεθόδου παρακολούθησης της δομικής ακεραιότητας για πληθυσμό ονομαστικά όμοιων αεροκατασκευών από σύνθετα υλικά υπό αβεβαιότητα λόγω μεταβαλλόμενων ΠΛΣ με επιμέρους στόχους:1.Ανάπτυξη μεθόδου ΠΔΑ βάση μηχανικής μάθησης ικανοποιώντας τις απαιτήσεις: (α)-(ε) που περιγράφονται παραπάνω.2.Διερεύνηση, μέσω αριθμητικών και εργαστηριακών πειραμάτων, των πλεονεκτημάτων της χρήσης δύο διαφορετικών ειδών αισθητηρίων μέτρησης: επιταχυνσιόμετρα ελαφρού τύπου και Fiber Bragg Grating (FBG). Διερεύνηση της συγχώνευσης πληροφορίας για μέγιστη διαγνωστική απόδοση.3.Επιβεβαίωση και αποτίμηση της αναπτυσσόμενης διαγνωστικής μεθόδου με εργαστηριακά πειράματα υπό μεταβαλλόμενες ΠΛΣ.4.Ανάπτυξη πρωτότυπου διαγνωστικού συστήματος.5.Έλεγχος και πειραματική αποτίμηση της απόδοσης του πρωτότυπου σύμφωνα με τις απαιτήσεις (α)-(ε). (Greek)
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The object of the project concerns the development and confirmation of a methodology and an original structural integrity monitoring system (MSM) of population nominally similar aero constructions from composite materials, installed in aircraft/unmanned vehicle flocks, based on machine learning and stochastic oscillation signals, under uncertainty due to changing environmental and operational conditions (MSS), achieving: High diagnostic performance, (b) robustness in changing PLS, (c) automated operation (minimum user intervention), (d) simplicity of equipment with the smallest possible number of sensors, and (e) training and operation with a small number of naturally available oscillation signals (without artificial stimulation) for possible in-flight application. The monitoring of structural integrity of aero constructions from composite materials is important for their safety and predictive maintenance, because they are subject to delamination, impact damage and other damage. Diagnostic systems based on naturally available oscillation signals are crucial for possible use on in-flight aircraft, offering simplicity, reliability and low cost. Since nominally similar aerial constructions are used in aircraft flocks, manufacturing population diagnosis is important for asset management facilitating the installation and operation of a diagnostic system, without the need for time-consuming adjustment procedures in each cluster construction. Achieving this under the above (a-e) requirements is expected to change the situation in industry. The main difficulty that needs to be overcome for efficient diagnosis in construction population is the fact that nominally similar aerial structures of composite materials never have absolutely similar properties due to variations in their construction, materials, and border conditions in the aircraft of the cluster. These lead to significant uncertainties among members of the population and jeopardise the correct diagnosis. The situation worsens when there is a combination, with also significant uncertainty caused by changing MPAs, a significant technological barrier in monitoring the structural integrity of individual structures. The beginning of this difficulty is found in the basic principle of operation of diagnostic methods based on oscillation measurements, where a failure is identified by changes it creates in the dynamics of construction, and which are recognised by the faint effects they cause on oscillation signals via advanced algorithms and microprocessors. The problem is that changes in MPAs can cause such changes in dynamics that they sometimes overlap, almost completely, changes due to faults and the diagnostic system does not achieve a reliable diagnosis. In the proposed project, which seeks to monitor structural integrity in construction population, the problem is wider and significantly more difficult. The project aims to develop a structural integrity monitoring method for population of nominally identical composite materials under uncertainty due to changing PPAs with individual objectives:1.Development of an EIA method based on machine learning by meeting the requirements: (a)-(e) described above.2.Investigation, through numerical and laboratory experiments, of the advantages of using two different kinds of measurement sensors: Light-type accelerometers and Fiber Bragg Grating (FBG). Investigation of information merging for maximum diagnostic performance.3.Confirming and evaluating the developing diagnostic method with laboratory experiments under changing PLS.4.Development of a prototype diagnostic system.5.Confirm and experimental evaluation of the performance of the prototype according to requirements (a)-(e). (English)
3 July 2021
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Identifiers
5.074.648
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