{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:28:00Z","timestamp":1778200080237,"version":"3.51.4"},"reference-count":63,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Accenture NeuroSHM project","award":["RC2302 2"],"award-info":[{"award-number":["RC2302 2"]}]},{"name":"Accenture NeuroSHM project","award":["21\/SPP\/3756"],"award-info":[{"award-number":["21\/SPP\/3756"]}]},{"name":"Science Foundation Ireland MaREI project","award":["RC2302 2"],"award-info":[{"award-number":["RC2302 2"]}]},{"name":"Science Foundation Ireland MaREI project","award":["21\/SPP\/3756"],"award-info":[{"award-number":["21\/SPP\/3756"]}]},{"name":"Science Foundation Ireland NexSys","award":["RC2302 2"],"award-info":[{"award-number":["RC2302 2"]}]},{"name":"Science Foundation Ireland NexSys","award":["21\/SPP\/3756"],"award-info":[{"award-number":["21\/SPP\/3756"]}]},{"name":"Enterprise Ireland SEMPRE","award":["RC2302 2"],"award-info":[{"award-number":["RC2302 2"]}]},{"name":"Enterprise Ireland SEMPRE","award":["21\/SPP\/3756"],"award-info":[{"award-number":["21\/SPP\/3756"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. In this regard, we show that spiking neural networks can be effectively used to extract cepstral coefficients as features of vibration signals of structures in their operational conditions. We demonstrate that the neural cepstral coefficients extracted by the network can be successfully used for anomaly detection. To address the power efficiency of sensor nodes, related to both processing and transmission, affecting the applicability of the proposed approach, we implement the algorithm on specialised neuromorphic hardware (Intel \u00ae Loihi architecture) and benchmark the results using numerical and experimental data of degradation in the form of stiffness change of a single degree of freedom system excited by Gaussian white noise. The work is expected to open a new direction of SHM applications towards non-Von Neumann computing through a neuromorphic approach.<\/jats:p>","DOI":"10.3390\/s22239245","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T08:13:09Z","timestamp":1669623189000},"page":"9245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Spiking Neural Networks for Structural Health Monitoring"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6757-0660","authenticated-orcid":false,"given":"George Vathakkattil","family":"Joseph","sequence":"first","affiliation":[{"name":"UCD Centre for Mechanics, Dynamical Systems and Risk Laboratory, School of Mechanical and Materials Engineering, University College Dublin, 4 Dublin, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8318-3521","authenticated-orcid":false,"given":"Vikram","family":"Pakrashi","sequence":"additional","affiliation":[{"name":"UCD Centre for Mechanics, Dynamical Systems and Risk Laboratory, School of Mechanical and Materials Engineering, University College Dublin, 4 Dublin, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1098\/rsta.2006.1928","article-title":"An introduction to structural health monitoring","volume":"365","author":"Farrar","year":"2007","journal-title":"Philos. 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