{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:01:42Z","timestamp":1776128502850,"version":"3.50.1"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Secretaria de Ciencia Humanidades Tecnologia e Innovacion","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008989","name":"Autonomous University of Queretaro","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008989","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013395","name":"Sistema Nacional de Investigadores","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013395","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electrical Engineering"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.compeleceng.2026.111137","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T05:25:47Z","timestamp":1774589147000},"page":"111137","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FPGA-based quaternion signal analysis of vibration signals for crack classification in wind turbine blades"],"prefix":"10.1016","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0405-5554","authenticated-orcid":false,"given":"Jose Luis","family":"Contreras-Hernandez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3373-0929","authenticated-orcid":false,"given":"Dora Luz","family":"Almanza-Ojeda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4317-0248","authenticated-orcid":false,"given":"Mario Alberto","family":"Ibarra-Manzano","sequence":"additional","affiliation":[]},{"given":"Arturo","family":"Garcia-Perez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3839-1396","authenticated-orcid":false,"given":"Martin","family":"Valtierra-Rodriguez","sequence":"additional","affiliation":[]},{"given":"David","family":"Granados-Lieberman","sequence":"additional","affiliation":[]},{"given":"David","family":"Camarena-Martinez","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"20","key":"10.1016\/j.compeleceng.2026.111137_b1","doi-asserted-by":"crossref","first-page":"7492","DOI":"10.3390\/en15207492","article-title":"Review on the damage and fault diagnosis of wind turbine blades in the germination stage","volume":"15","author":"Song","year":"2022","journal-title":"Energies"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111137_b2","doi-asserted-by":"crossref","DOI":"10.1088\/0964-1726\/24\/3\/033001","article-title":"A review of damage detection methods for wind turbine blades","volume":"24","author":"Li","year":"2015","journal-title":"Smart Mater Struct"},{"key":"10.1016\/j.compeleceng.2026.111137_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2022.112723","article-title":"Recent advances in damage detection of wind turbine blades: A state-of-the-art review","volume":"167","author":"Kaewniam","year":"2022","journal-title":"Renew Sustain Energy Rev"},{"key":"10.1016\/j.compeleceng.2026.111137_b4","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1016\/j.jsv.2013.11.015","article-title":"On damage diagnosis for a wind turbine blade using pattern recognition","volume":"333","author":"Dervilis","year":"2014","journal-title":"J Sound Vib"},{"key":"10.1016\/j.compeleceng.2026.111137_b5","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1016\/j.renene.2022.09.032","article-title":"A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods","volume":"199","author":"Khazaee","year":"2022","journal-title":"Renew Energy"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111137_b6","article-title":"A methodological approach for detecting multiple faults in wind turbine blades based on vibration signals and machine learning","volume":"10","author":"Ogaili","year":"2023","journal-title":"Curved Layer Struct"},{"issue":"8","key":"10.1016\/j.compeleceng.2026.111137_b7","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ac68d0","article-title":"Acoustic-based method for identifying surface damage to wind turbine blades by using a convolutional neural network","volume":"33","author":"Tsai","year":"2022","journal-title":"Meas Sci Technol"},{"key":"10.1016\/j.compeleceng.2026.111137_b8","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3514747","article-title":"Condition monitoring and fault diagnosis of wind turbine: A systematic literature review","volume":"12","author":"Hussain","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.compeleceng.2026.111137_b9","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.measurement.2019.01.088","article-title":"Motor fault detection using Quaternion Signal Analysis on FPGA","volume":"138","author":"Contreras-Hernandez","year":"2019","journal-title":"Measurement"},{"key":"10.1016\/j.compeleceng.2026.111137_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111179","article-title":"Quaternion empirical wavelet transform and its applications in rolling bearing fault diagnosis","volume":"195","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.compeleceng.2026.111137_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2026.104397","article-title":"Robust and interpretable bearing fault diagnosis via multi-scale differential-enhanced convolution with dual-dimensional redundancy suppression and dynamic quaternion transformer","volume":"71","author":"Chen","year":"2026","journal-title":"Adv Eng Informatics"},{"issue":"23","key":"10.1016\/j.compeleceng.2026.111137_b12","doi-asserted-by":"crossref","first-page":"12622","DOI":"10.3390\/app132312622","article-title":"Short-circuit damage diagnosis in transformer windings using quaternions: Severity assessment through current and vibration signals","volume":"13","author":"Contreras-Hernandez","year":"2023","journal-title":"Appl Sci"},{"issue":"5","key":"10.1016\/j.compeleceng.2026.111137_b13","doi-asserted-by":"crossref","first-page":"663","DOI":"10.3390\/mi13050663","article-title":"FPGA implementation of AI-based inverter IGBT open circuit fault diagnosis of induction motor drives","volume":"13","author":"Rajeswaran","year":"2022","journal-title":"Micromachines"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111137_b14","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3390\/electronics13010072","article-title":"Fault classification and diagnosis approach using FFT-CNN for FPGA-based CORDIC processor","volume":"13","author":"Xie","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.compeleceng.2026.111137_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2022.108111","article-title":"A monitoring and diagnostics method based on FPGA-digital twin for power electronic transformers","volume":"210","author":"Xiong","year":"2022","journal-title":"Electr Power Syst Res"},{"key":"10.1016\/j.compeleceng.2026.111137_b16","doi-asserted-by":"crossref","first-page":"2622","DOI":"10.3390\/s22072622","article-title":"Geometric analysis of signals for inference of multiple faults in induction motors","volume":"22","author":"Contreras-Hernandez","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.compeleceng.2026.111137_b17","series-title":"Digital Signal Processing: A Computer-based Approach","author":"Mitra","year":"2011"},{"key":"10.1016\/j.compeleceng.2026.111137_b18","series-title":"Wireless Communications from the Ground Up: An SDR Perspective","author":"Chaudhari","year":"2018"},{"key":"10.1016\/j.compeleceng.2026.111137_b19","series-title":"Digital signal processing","first-page":"241","article-title":"IIR digital filter design","author":"Rao","year":"2018"},{"key":"10.1016\/j.compeleceng.2026.111137_b20","series-title":"Infinite Impulse Response Filters. In Digital Signal Processing. An Introduction","first-page":"289","author":"Sundararajan","year":"2024"},{"key":"10.1016\/j.compeleceng.2026.111137_b21","unstructured":"Voight J. Quaternion Algebras. Springer Cham; p. 2018."},{"key":"10.1016\/j.compeleceng.2026.111137_b22","series-title":"Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace, and Virtual Reality","author":"Kuipers","year":"1999"},{"key":"10.1016\/j.compeleceng.2026.111137_b23","first-page":"471","article-title":"Fault prediction model in wind turbines using deep learning structure with enhanced optimization algorithm","volume":"1","author":"Gawalia","year":"2023","journal-title":"J Control Decis"},{"key":"10.1016\/j.compeleceng.2026.111137_b24","series-title":"Tree in tree: from decision trees to decision graphs","author":"Zhu","year":"2021"},{"key":"10.1016\/j.compeleceng.2026.111137_b25","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TC.2019.2916817","article-title":"Low-power unsigned divider and square root circuit designs using adaptive approximation","volume":"68","author":"Jiang","year":"2019","journal-title":"IEEE Trans Comput"},{"key":"10.1016\/j.compeleceng.2026.111137_b26","doi-asserted-by":"crossref","first-page":"72","DOI":"10.54097\/3cq7qb95","article-title":"Signal processing based on butterworth filter: Properties, design, and applications","volume":"97","author":"Wang","year":"2024","journal-title":"Highlights Sci Eng Technol"},{"key":"10.1016\/j.compeleceng.2026.111137_b27","series-title":"Evaluation metrics in learning systems: a survey","author":"Nouri","year":"2025"}],"container-title":["Computers and Electrical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790626002090?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790626002090?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:24:36Z","timestamp":1776126276000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0045790626002090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":27,"alternative-id":["S0045790626002090"],"URL":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111137","relation":{},"ISSN":["0045-7906"],"issn-type":[{"value":"0045-7906","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FPGA-based quaternion signal analysis of vibration signals for crack classification in wind turbine blades","name":"articletitle","label":"Article Title"},{"value":"Computers and Electrical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111137","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111137"}}