{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T12:08:19Z","timestamp":1775909299797,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deutsche Forschungsgemeinschaft (DFG\u2014German Research Foundation)"},{"name":"Open-Access Publishing Fund of Technical University of Darmstadt"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning methods are becoming increasingly popular, aiming for accurate predictions of bearing faults without requiring bearing-specific domain knowledge. Opposing this trend in popularity, the present paper takes a more traditional approach, incorporating domain knowledge by evaluating a variety of feature engineering methods in combination with a random forest classifier. For a comprehensive feature engineering study, a total of 42 mathematical feature formulas are combined with the preprocessing methods of envelope analysis, empirical mode decomposition, wavelet transforms, and frequency band separations. While each single processing method and feature formula is known from the literature, the presented paper contributes to the body of knowledge by investigating novel series connections of processing methods and feature formulas. Using the CWRU bearing fault data for performance evaluation, feature calculation based on the processing method of frequency band separation leads to particularly high prediction accuracies, while at the same time being very efficient in terms of low computational effort. Additionally, in comparison with deep learning approaches, the proposed feature engineering method provides excellent accuracies and enables explainability.<\/jats:p>","DOI":"10.3390\/e25091278","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T09:14:41Z","timestamp":1693473281000},"page":"1278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7989-1293","authenticated-orcid":false,"given":"Christoph","family":"Bienefeld","sequence":"first","affiliation":[{"name":"Institute for Product Development and Machine Elements, Technical University of Darmstadt, Otto-Berndt-Stra\u00dfe 2, 64287 Darmstadt, Germany"},{"name":"Bosch Center for AI, Corporate Research, Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272 Renningen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4272-861X","authenticated-orcid":false,"given":"Florian Michael","family":"Becker-Dombrowsky","sequence":"additional","affiliation":[{"name":"Institute for Product Development and Machine Elements, Technical University of Darmstadt, Otto-Berndt-Stra\u00dfe 2, 64287 Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Etnik","family":"Shatri","sequence":"additional","affiliation":[{"name":"Institute for Product Development and Machine Elements, Technical University of Darmstadt, Otto-Berndt-Stra\u00dfe 2, 64287 Darmstadt, Germany"},{"name":"Bosch Center for AI, Corporate Research, Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272 Renningen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7663-8073","authenticated-orcid":false,"given":"Eckhard","family":"Kirchner","sequence":"additional","affiliation":[{"name":"Institute for Product Development and Machine Elements, Technical University of Darmstadt, Otto-Berndt-Stra\u00dfe 2, 64287 Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","unstructured":"Vorwerk-Handing, G., Martin, G., and Kirchner, E. 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