{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T13:11:28Z","timestamp":1788873088469,"version":"build-2803163510"},"reference-count":31,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006147","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC07-05ID14517"],"award-info":[{"award-number":["DE-AC07-05ID14517"]}],"id":[{"id":"10.13039\/100006147","id-type":"DOI","asserted-by":"publisher"}]},{"award":["DE-AC07-05ID14517"],"award-info":[{"award-number":["DE-AC07-05ID14517"]}],"id":[{"id":"https:\/\/ror.org\/01bj3aw27","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Acoustic emission (AE) sensing is widely used for structural health monitoring, but conventional piezoelectric sensors can be constrained in high-temperature and radiation environments. This study evaluates whether an Idaho National Laboratory-developed magnetostrictive AE sensor retains sufficient information for automated impact-source classification under a controlled room-temperature configuration. A total of 334 synchronized acquisitions from six impact classes were recorded at 20 MHz using three fixed, non-coincident sensor channels. Because sensor type was not independently varied from position, mounting, coupling, bandwidth, or propagation path, comparisons represent complete sensor-channel responses rather than isolated transduction mechanisms. Each waveform was represented by 16 time-domain, spectral, and band-power features. Random forest classifiers were evaluated for individual channels, 48-feature fusion, and equal-vote decision fusion using 30 repeated stratified holdout runs and stratified 10-fold cross-validation. The magnetostrictive channel achieved 95.35 \u00b1 2.45% mean holdout accuracy, compared with 92.37 \u00b1 2.94% and 96.31 \u00b1 1.98% for the two commercial channels. Feature-level fusion achieved 97.12 \u00b1 2.19% and 97.91 \u00b1 3.18% under holdout and 10-fold cross-validation, respectively. Because class-specific blocks were split at the event level, these accuracies are within-campaign and within-specimen, may be optimistic, and do not establish block-independent or operational generalization; validation using randomized or interleaved repeated blocks, multiple specimens, position-controlled comparisons with swapped, rotated, or co-located sensor placements, and harsh-environment testing remains necessary.<\/jats:p>","DOI":"10.3390\/s26185693","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:51:48Z","timestamp":1788871908000},"page":"5693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine-Learning Evaluation of a Magnetostrictive Acoustic-Emission Sensor Developed for Nuclear Structural Health Monitoring"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8720-3481","authenticated-orcid":false,"given":"Bibo","family":"Zhong","sequence":"first","affiliation":[{"name":"Idaho National Laboratory, Idaho Falls, ID 83415, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9863-6336","authenticated-orcid":false,"given":"Chaitee Milind","family":"Godbole","sequence":"additional","affiliation":[{"name":"Idaho National Laboratory, Idaho Falls, ID 83415, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1334-0509","authenticated-orcid":false,"given":"Vivek","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Idaho National Laboratory, Idaho Falls, ID 83415, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4377-6231","authenticated-orcid":false,"given":"Joshua E.","family":"Daw","sequence":"additional","affiliation":[{"name":"Idaho National Laboratory, Idaho Falls, ID 83415, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1088\/0022-3735\/20\/8\/001","article-title":"An Introduction to Acoustic-Emission","volume":"20","author":"Scruby","year":"1987","journal-title":"J. Phys. E Sci. Instrum."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s11340-015-9984-5","article-title":"Identification of Crack Initiation in Aluminum Alloys using Acoustic Emission","volume":"55","author":"Vanniamparambil","year":"2015","journal-title":"Exp. Mech."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108039","DOI":"10.1016\/j.compositesb.2020.108039","article-title":"Damage characterization of laminated composites using acoustic emission: A review","volume":"195","author":"Saeedifar","year":"2020","journal-title":"Compos. Part B Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Grosse, C.U., Ohtsu, M., Aggelis, D.G., and Shiotani, T. (2022). Acoustic Emission Testing: Basics for Research\u2013Applications in Engineering, Springer. [2nd ed.].","DOI":"10.1007\/978-3-030-67936-1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100294","DOI":"10.1016\/j.dibe.2023.100294","article-title":"Structural health monitoring of stainless-steel nuclear fuel storage canister using acoustic emission","volume":"17","author":"Ai","year":"2024","journal-title":"Dev. Built Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S0963-8695(98)00077-2","article-title":"Modal analysis of acoustic emission signals from CFRP laminates","volume":"32","author":"Surgeon","year":"1999","journal-title":"NDT E Int."},{"key":"ref_7","first-page":"39","article-title":"A wavelet transform applied to acoustic emission","volume":"20","author":"Hamstad","year":"2002","journal-title":"J. Acoust. Emiss."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ciaburro, G., and Iannace, G. (2022). Machine-Learning-Based Methods for Acoustic Emission Testing: A Review. Appl. Sci., 12.","DOI":"10.3390\/app122010476"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/s10853-022-08063-1","article-title":"Review on acoustic emission source location, damage recognition and lifetime prediction of fiber-reinforced composites","volume":"58","author":"Zhou","year":"2023","journal-title":"J. Mater. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/TNS.2017.2775163","article-title":"Irradiation Testing of Piezoelectric (Aluminum Nitride, Zinc Oxide, and Bismuth Titanate) and Magnetostrictive Sensors (Remendur and Galfenol)","volume":"65","author":"Reinhardt","year":"2018","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhong, B., and Daw, J. (2025). High-Temperature Magnetostrictive Acoustic Emission Transducer for Fuel Pin Burst Detection. Proceedings of the Nuclear Plant Instrumentation and Control and Human-Machine Interface Technology (NPIC&HMIT 2025), Chicago, IL, USA, 15\u201318 June 2025, American Nuclear Society.","DOI":"10.13182\/xyz-46625"},{"key":"ref_12","unstructured":"Zhong, B., and Daw, J.E. (2025). Enhanced Design of Radiation Tolerant High-Temperature Structural Health Monitoring Sensors, Idaho National Laboratory (INL)."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1177\/1475921718791894","article-title":"Investigation of damage detectability in composites using frequency-based classification of acoustic emission measurements","volume":"18","author":"Wirtz","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Amevorku, R.D., Amoateng-Mensah, D., Rijal, M., and Sundaresan, M.J. (2025). Classification and Clustering of Fiber Break Events in Thermoset CFRP Using Acoustic Emission and Machine Learning. Sensors, 25.","DOI":"10.3390\/s25206466"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109450","DOI":"10.1016\/j.compositesb.2021.109450","article-title":"Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel","volume":"228","author":"Sikdar","year":"2022","journal-title":"Compos. Part B Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1177\/1045389X241291439","article-title":"Data level fusion of acoustic emission sensors using deep learning","volume":"36","author":"Cheng","year":"2025","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108981","DOI":"10.1016\/j.ymssp.2022.108981","article-title":"Developing a heterogeneous ensemble learning framework to evaluate Alkali-silica reaction damage in concrete using acoustic emission signals","volume":"172","author":"Ai","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107083","DOI":"10.1016\/j.engfracmech.2020.107083","article-title":"Application of different acoustic emission descriptors in damage assessment of fiber reinforced plastics: A comprehensive review","volume":"235","author":"Barile","year":"2020","journal-title":"Eng. Fract. Mech."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Godin, N., Reynaud, P., and Fantozzi, G. (2018). Challenges and Limitations in the Identification of Acoustic Emission Signature of Damage Mechanisms in Composites Materials. Appl. Sci., 8.","DOI":"10.3390\/app8081267"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"May, Z., Alam, M.K., Nayan, N.A., Rahman, N.A.A., and Mahmud, M.S. (2021). Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0261040"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.patrec.2011.09.018","article-title":"Pattern recognition approach to identify natural clusters of acoustic emission signals","volume":"33","author":"Sause","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108425","DOI":"10.1016\/j.apacoust.2021.108425","article-title":"Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique","volume":"185","author":"Barile","year":"2022","journal-title":"Appl. Acoust."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Brusamarello, B., Dreyer, U.J., Brunetto, G.A., Pedrozo Melegari, L.F., Martelli, C., and Cardozo da Silva, J.C. (2024). Multilayer Structure Damage Detection Using Optical Fiber Acoustic Sensing and Machine Learning. Sensors, 24.","DOI":"10.3390\/s24175777"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shi, S., Yao, D., Wu, G., Chen, H., and Zhang, S. (2024). Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods. Sensors, 24.","DOI":"10.3390\/s24010275"},{"key":"ref_27","unstructured":"Zhang, Y. (2024). Tiny Machine Learning for Structural Health Monitoring with Acoustic Emissions. [Licentiate Thesis, Mid Sweden University]. Mid Sweden University Licentiate Thesis No. 204."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ebrahimkhanlou, A., and Salamone, S. (2018). Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning. Aerospace, 5.","DOI":"10.3390\/aerospace5020050"},{"key":"ref_29","unstructured":"MISTRAS Group, Inc (2026, August 30). D9215 Sensor: High Temperature Sensor\u2014Product Data Sheet. Available online: https:\/\/www.physicalacoustics.com\/content\/literature\/sensors\/Model_D9215.pdf."},{"key":"ref_30","unstructured":"MISTRAS Group, Inc (2026, August 30). WD Sensor: Wideband Differential Sensor\u2014Product Data Sheet. Available online: https:\/\/www.physicalacoustics.com\/content\/literature\/sensors\/Model_WD.pdf."},{"key":"ref_31","unstructured":"(2021). Standard Terminology for Nondestructive Examinations (Standard No. ASTM E1316-21)."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5693\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:54:45Z","timestamp":1788872085000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,8]]},"references-count":31,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185693"],"URL":"https:\/\/doi.org\/10.3390\/s26185693","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,8]]}}}