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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:28:18Z","timestamp":1724459298440},"reference-count":50,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T00:00:00Z","timestamp":1724198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMSs) perform real-time monitoring functions, but their performance must be carefully assessed. This is typically done by introducing artificial damages to the components; however, such a procedure requires the production and testing of a large number of structural elements. In this work, the damage detection performance of a strain-based SHMS was evaluated on a composite helicopter rotor blade root, exploiting a Finite Element (FE) model of the component. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through FE analyses. The effects of the load\u2019s variability and of the damage were decoupled by including a load recognition step in the algorithm, which was accomplished either with an Artificial Neural Network (ANN) or a calibration matrix. Anomaly detection, damage assessment, and localization were performed by using an ANN. The results showed a higher load identification and anomaly detection accuracy using an ANN for the load recognition, and the load set was recognized with a satisfactory accuracy, even in damaged blades. This case study was focused on a real-world subcomponent with complex geometrical features and realistic load conditions, which was not investigated in the literature and provided a promising approach to estimate the performance of a strain-based SHMS.<\/jats:p>","DOI":"10.3390\/s24165411","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T16:58:07Z","timestamp":1724432287000},"page":"5411","source":"Crossref","is-referenced-by-count":0,"title":["Application of Artificial Neural Networks to a Model of a Helicopter Rotor Blade for Damage Identification in Realistic Load Conditions"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0631-5746","authenticated-orcid":false,"given":"Pietro","family":"Ballarin","sequence":"first","affiliation":[{"name":"Department of Aerospace Science and Technology, Politecnico di Milano, 20156 Milan, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1997-6073","authenticated-orcid":false,"given":"Giuseppe","family":"Sala","sequence":"additional","affiliation":[{"name":"Department of Aerospace Science and Technology, Politecnico di Milano, 20156 Milan, Italy"}]},{"given":"Marco","family":"Macchi","sequence":"additional","affiliation":[{"name":"Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20156 Milan, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7795-1611","authenticated-orcid":false,"given":"Irene","family":"Roda","sequence":"additional","affiliation":[{"name":"Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20156 Milan, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0009-0009-0217-8746","authenticated-orcid":false,"given":"Andrea","family":"Baldi","sequence":"additional","affiliation":[{"name":"Leonardo S.p.A., Helicopters Division, 21017 Cascina Costa di Samarate, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4938-3407","authenticated-orcid":false,"given":"Alessandro","family":"Airoldi","sequence":"additional","affiliation":[{"name":"Department of Aerospace Science and Technology, Politecnico di Milano, 20156 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"ref_1","unstructured":"Bottasso, L.M., Sala, G., Bettini, P., Tagliabue, P., Corbani, F., Platini, E., Guerra, A., and Anelli, A. 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