{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T14:24:47Z","timestamp":1790778287428,"version":"4.1.0"},"reference-count":35,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry MCIN\/AEI\/10.13039\/501100011033","award":["PID2020-114686RB-I00"],"award-info":[{"award-number":["PID2020-114686RB-I00"]}]},{"name":"Spanish Ministry MCIN\/AEI\/10.13039\/501100011033","award":["TwinGrind (RTC2019-007064-2)"],"award-info":[{"award-number":["TwinGrind (RTC2019-007064-2)"]}]},{"name":"Spanish Science and Innovation Ministry","award":["PID2020-114686RB-I00"],"award-info":[{"award-number":["PID2020-114686RB-I00"]}]},{"name":"Spanish Science and Innovation Ministry","award":["TwinGrind (RTC2019-007064-2)"],"award-info":[{"award-number":["TwinGrind (RTC2019-007064-2)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grinding wheel wear status monitoring using an AE sensor. The most relevant finding is the possibility of efficient feature extraction form frequency plots using CNNs. Feature extraction from FFT plots requires sound domain-expert knowledge, and thus we present a new approach to automated feature extraction using a pre-trained CNN. Using the features extracted for different industrial grinding conditions, t-SNE and PCA clustering algorithms were tested for wheel wear state identification. Results are compared for different industrial grinding conditions. The initial state of the wheel, resulting from the dressing operation, is clearly identified for all the experiments carried out. This is a very important finding, since dressing strongly affects operation performance. When grinding parameters produce acute wear of the wheel, the algorithms show very good clustering performance using the features extracted by the CNN. Performance of both t-SNE and PCA was very much the same, thus confirming the excellent efficiency of the pre-trained CNN for automated feature extraction from FFT plots.<\/jats:p>","DOI":"10.3390\/s22186911","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T22:37:28Z","timestamp":1663108648000},"page":"6911","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels"],"prefix":"10.3390","volume":"22","author":[{"given":"D.","family":"Gonz\u00e1lez","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV\/EHU), 48013 Bilbao, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4031-9281","authenticated-orcid":false,"given":"J.","family":"Alvarez","sequence":"additional","affiliation":[{"name":"Ideko Centro Tecnol\u00f3gico, Basque Research and Technology Alliance (BRTA), 20870 Elgoibar, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1187-0207","authenticated-orcid":false,"given":"J. A.","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV\/EHU), 48013 Bilbao, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0137-2995","authenticated-orcid":false,"given":"L.","family":"Godino","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV\/EHU), 48013 Bilbao, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I.","family":"Pombo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV\/EHU), 48013 Bilbao, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rcim.2016.10.004","article-title":"Machine-vision-based identification of broken inserts in edge profile milling heads","volume":"44","author":"Azzopardi","year":"2017","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1016\/j.wear.2017.02.017","article-title":"Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System","volume":"376\u2013377","author":"Rizal","year":"2017","journal-title":"Wear"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.cirp.2019.03.010","article-title":"A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning","volume":"68","author":"Li","year":"2019","journal-title":"CIRP Ann."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101859","DOI":"10.1016\/j.rcim.2019.101859","article-title":"In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems","volume":"61","author":"Lins","year":"2020","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101924","DOI":"10.1016\/j.rcim.2019.101924","article-title":"In-process tool condition forecasting based on a deep learning method","volume":"64","author":"Sun","year":"2020","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.cirp.2019.03.007","article-title":"Heterogeneous data-driven hybrid machine learning for tool condition prognosis","volume":"68","author":"Wang","year":"2019","journal-title":"CIRP Ann."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.cirp.2016.06.003","article-title":"Abrasive fine-finishing technology","volume":"65","author":"Hashimoto","year":"2016","journal-title":"CIRP Ann."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.jmapro.2019.07.006","article-title":"The effect of radial infeed on surface integrity in dry generating gear grinding for industrial production of automotive transmission gears","volume":"45","author":"Guerrini","year":"2019","journal-title":"J. Manuf. Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106144","DOI":"10.1016\/j.triboint.2019.106144","article-title":"Tool wear behavior of vitrified microcrystalline alumina wheels in creep feed profile grinding of turbine blade root of single crystal nickel-based superalloy","volume":"145","author":"Miao","year":"2020","journal-title":"Tribol. Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105921","DOI":"10.1016\/j.ijmecsci.2020.105921","article-title":"Study on surface generation mechanism and roughness distribution in gear profile grinding","volume":"187","author":"Zhou","year":"2020","journal-title":"Int. J. Mech. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.cirp.2015.05.004","article-title":"Abrasive machining of advanced aerospace alloys and composites","volume":"64","author":"Klocke","year":"2015","journal-title":"CIRP Ann."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109637","DOI":"10.1016\/j.vacuum.2020.109637","article-title":"Effect of grinding parameters on surface quality, microstructure and rolling contact fatigue behaviors of gear steel for vacuum pump","volume":"180","author":"Kang","year":"2020","journal-title":"Vacuum"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"203475","DOI":"10.1016\/j.wear.2020.203475","article-title":"Wear mechanism in high-speed superabrasive grinding of titanium alloy and its effect on surface integrity","volume":"462\u2013463","author":"Naskar","year":"2020","journal-title":"Wear"},{"key":"ref_14","first-page":"1","article-title":"Caracterizaci\u00f3n experimental del desgaste radial de muelas de rectificado con aglomerante v\u00edtreo","volume":"25","author":"Pazmino","year":"2021","journal-title":"Rev. Iberoam. Ing. Mec."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/S0007-8506(07)61700-4","article-title":"Process Monitoring in Grinding","volume":"51","author":"Friemuth","year":"2002","journal-title":"CIRP Ann."},{"key":"ref_16","unstructured":"Kaiser, J. (1950). Untersuchungen \u00fcber das Auftreten Ger\u00e4uschen beim Zugversuch, Technische Hochschule M\u00fcnchen."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.precisioneng.2021.01.009","article-title":"A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform","volume":"70","author":"Mahata","year":"2021","journal-title":"Precis. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/S0007-8506(07)62108-8","article-title":"Application of AE Contact Sensing in Reliable Grinding Monitoring","volume":"50","author":"Dornfeld","year":"2001","journal-title":"CIRP Ann."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106365","DOI":"10.1016\/j.ymssp.2019.106365","article-title":"A novel feature representation method based on original waveforms for acoustic emission signals","volume":"135","author":"Yang","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zeinali, H., Stafylakis, T., Athanasopoulou, G., Rohdin, J., Gkinis, I., Burget, L., and \u010cernock\u00fd, J. (2019). Detecting spoofing attacks using VGG and SincNet: BUT-omilia submission to ASVspoof 2019 challenge. Proc. Interspeech, 1073\u20131077.","DOI":"10.21437\/Interspeech.2019-2892"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107094","DOI":"10.1016\/j.compeleceng.2021.107094","article-title":"Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine","volume":"92","author":"Abbasi","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/S0924-0136(00)00808-6","article-title":"An intelligent system for grinding wheel condition monitoring","volume":"109","author":"Lezanski","year":"2001","journal-title":"J. Mater. Process. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1784\/204764213808146626","article-title":"Vibration-based fault diagnosis in rolling element bearings: Ranking of various time, frequency and time-frequency domain data-based damage identi cation parameters","volume":"3","author":"Shakya","year":"2013","journal-title":"Int. J. Cond. Monit."},{"key":"ref_24","unstructured":"Penaranda, K.G. (2016). Pattern Recognition, Classification and Diagnosis of Acoustic Emission Signals in Applications for Mining, RWTH Aachen University."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.ijmachtools.2006.05.008","article-title":"A wavelet-based methodology for grinding wheel condition monitoring","volume":"47","author":"Ting","year":"2007","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.engappai.2009.09.004","article-title":"Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring","volume":"23","author":"Liao","year":"2010","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s00170-011-3797-1","article-title":"Grinding wheel wear monitoring based on wavelet analysis and support vector machine","volume":"62","author":"Yang","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7026","DOI":"10.1016\/j.eswa.2015.05.008","article-title":"Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics","volume":"42","author":"Nakai","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109864","DOI":"10.1016\/j.measurement.2021.109864","article-title":"Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform","volume":"183","author":"Wei","year":"2021","journal-title":"Measurement"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104814","DOI":"10.1016\/j.compbiomed.2021.104814","article-title":"Classification of heart sounds based on quality assessment and wavelet scattering transform","volume":"137","author":"Mei","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1504\/IJAT.2009.021636","article-title":"Characteristics of the acoustic emission during horizontal single grit scratch tests: Part 1 characteristics and identification","volume":"2","author":"Griffin","year":"2009","journal-title":"Int. J. Abras. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","unstructured":"(2022, September 01). Mathworks. Available online: https:\/\/es.mathworks.com\/help\/deeplearning\/ref\/resnet18.html."},{"key":"ref_34","first-page":"2579","article-title":"Visualizing Data Using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","first-page":"2033","article-title":"Stochastic neighbor embedding","volume":"15","author":"Hinton","year":"2002","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6911\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:30:31Z","timestamp":1760142631000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6911"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,13]]},"references-count":35,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186911"],"URL":"https:\/\/doi.org\/10.3390\/s22186911","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,13]]}}}