{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T11:57:21Z","timestamp":1777118241872,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals\/features becomes an arduous task, leading the specialist to rely often on general alarms, which in turn can compromise the accuracy of the diagnosis. In order to make monitoring more intelligent, several machine learning techniques have been proposed to reduce the dimension of the input data and also to analyze it. This paper, therefore, aims to compare the use of vibration features extracted based on machine learning models, expert domain, and other signal processing approaches for identifying bearing faults (anomalies) using machine learning (ML)\u2014in addition to verifying the possibility of reducing the number of monitored features, and consequently the behavior of the model when working with reduced dimensionality of the input data. As vibration analysis is one of the predictive techniques that present better results in the monitoring of rotating machinery, vibration signals from an experimental bearing dataset were used. The proposed features were used as input to an unsupervised anomaly detection model (Isolation Forest) to identify bearing fault. Through the study, it is possible to verify how the ML model behaves in view of the different possibilities of input features used, and their influences on the final result in addition to the possibility of reducing the number of features that are usually monitored by reducing the dimension. In addition to increasing the accuracy of the model when extracting correct features for the application under study, the reduction in dimensionality allows the specialist to monitor in a compact way the various features collected on the equipment.<\/jats:p>","DOI":"10.3390\/informatics8040085","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T05:02:36Z","timestamp":1638334956000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Fault Detection of Bearing: An Unsupervised Machine Learning Approach Exploiting Feature Extraction and Dimensionality Reduction"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5289-0840","authenticated-orcid":false,"given":"Lucas Costa","family":"Brito","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Federal University of Uberl\u00e2ndia, Av. Jo\u00e3o N. Avila, Uberl\u00e2ndia 38408-100, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5739-9639","authenticated-orcid":false,"given":"Gian Antonio","family":"Susto","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, Via Gradenigo 6\/B, 35131 Padova, Italy"}]},{"given":"Jorge Nei","family":"Brito","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Federal University of S\u00e3o Jo\u00e3o del Rei, P. Orlando, S\u00e3o Jo\u00e3o del Rei 36300-000, Brazil"}]},{"given":"Marcus Antonio Viana","family":"Duarte","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Federal University of Uberl\u00e2ndia, Av. Jo\u00e3o N. Avila, Uberl\u00e2ndia 38408-100, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ymssp.2012.09.015","article-title":"A review on empirical mode decomposition in fault diagnosis of rotating machinery","volume":"35","author":"Lei","year":"2013","journal-title":"Mech. Syst. Signal Process"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1007\/s10845-015-1179-5","article-title":"Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance","volume":"29","author":"Bousdekis","year":"2018","journal-title":"J. Intell. Manufact."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.renene.2018.10.047","article-title":"Machine learning methods for wind turbine condition monitoring: A review","volume":"133","author":"Stetco","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_5","unstructured":"Zocco, F., Maggipinto, M., Susto, G.A., and McLoone, S. (2021, October 21). Greedy Search Algorithms for Unsupervised Variable Selection: A Comparative Study, Available online: http:\/\/xxx.lanl.gov\/abs\/2103.02687."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1109\/TIE.2017.2762623","article-title":"Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines","volume":"65","author":"Ciabattoni","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wei, Y., Li, Y., Xu, M., and Huang, W. (2019). A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery. Entropy, 21.","DOI":"10.3390\/e21040409"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108105","DOI":"10.1016\/j.ymssp.2021.108105","article-title":"An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery","volume":"163","author":"Brito","year":"2022","journal-title":"Mech. Syst. Signal Process"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. (1986). Principal Component Analysis, Springer.","DOI":"10.1007\/978-1-4757-1904-8"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Strange, H., and Zwiggelaar, R. (2014). Open Problems in Spectral Dimensionality Reduction, Springer. Springer Briefs in Computer Science.","DOI":"10.1007\/978-3-319-03943-5"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"Tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref_12","first-page":"2579","article-title":"Visualizing Data Using t-SNE","volume":"9","author":"VanDerMaaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-1684(91)90079-X","article-title":"Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture","volume":"24","author":"Jutten","year":"1991","journal-title":"Signal Process."},{"key":"ref_14","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_15","first-page":"1","article-title":"PyOD: A Python Toolbox for Scalable Outlier Detection","volume":"20","author":"Zhao","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hawkins, D. (1980). Identification of Outliers, Chapman and Hall.","DOI":"10.1007\/978-94-015-3994-4"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Barbariol, T., Feltresi, E., and Susto, G.A. (2020). Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection. Energies, 13.","DOI":"10.3390\/en13123136"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 5\u201319). Isolation forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10115-012-0487-8","article-title":"A review of feature selection methods on synthetic data","volume":"34","year":"2013","journal-title":"Knowl. Inf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1016\/j.neucom.2011.03.043","article-title":"Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks","volume":"74","author":"Zhang","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2426","DOI":"10.1016\/j.neucom.2017.11.016","article-title":"A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method","volume":"275","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1016\/j.ymssp.2009.01.009","article-title":"Gear crack level identification based on weighted K nearest neighbor classification algorithm","volume":"23","author":"Lei","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.ymssp.2016.12.040","article-title":"A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection","volume":"91","author":"Li","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.measurement.2018.09.013","article-title":"Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine","volume":"131","author":"Singh","year":"2019","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","article-title":"Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study","volume":"64\u201365","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Diallo, M., Mokeddem, S., Braud, A., Frey, G., and Lachiche, N. (2021). Identifying Benchmarks for Failure Prediction in Industry 4.0. Informatics, 8.","DOI":"10.3390\/informatics8040068"},{"key":"ref_27","unstructured":"Lee, J., Qiu, H., Yu, G., and Lin, J. (2021, November 14). Bearing Dataset. IMS; University of Cincinnati, NASA Ames Prognostics Data Repository, Rexnord Technical Services: Moffett Field, CA, USA, 2007, Available online: https:\/\/ti.arc.nasa.gov\/tech\/dash\/pcoe\/prognostic-data-repository\/#bearing."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.jsv.2005.03.007","article-title":"Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics","volume":"289","author":"Qiu","year":"2006","journal-title":"J. Sound Vib."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/8\/4\/85\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:36Z","timestamp":1760168136000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/8\/4\/85"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,25]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["informatics8040085"],"URL":"https:\/\/doi.org\/10.3390\/informatics8040085","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,25]]}}}