{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T21:44:54Z","timestamp":1777326294116,"version":"3.51.4"},"reference-count":88,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["308712\/2019-6"],"award-info":[{"award-number":["308712\/2019-6"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","award":["72200158"],"award-info":[{"award-number":["72200158"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy\/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features\u2019 importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.<\/jats:p>","DOI":"10.3390\/s21175888","type":"journal-article","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T11:39:03Z","timestamp":1630496343000},"page":"5888","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9701-8124","authenticated-orcid":false,"given":"Joaqu\u00edn","family":"Figueroa Barraza","sequence":"first","affiliation":[{"name":"LabRisco\u2014Analysis, Evaluation and Risk Management Laboratory, Department of Naval Architecture and Ocean Engineering, University of S\u00e3o Paulo, S\u00e3o Paulo 05508-030, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0790-8439","authenticated-orcid":false,"given":"Enrique","family":"L\u00f3pez Droguett","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering & The Garrick Institute for the Risk Sciences, University of California, Los Angeles, CA 90095, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-4437","authenticated-orcid":false,"given":"Marcelo Ramos","family":"Martins","sequence":"additional","affiliation":[{"name":"LabRisco\u2014Analysis, Evaluation and Risk Management Laboratory, Department of Naval Architecture and Ocean Engineering, University of S\u00e3o Paulo, S\u00e3o Paulo 05508-030, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,1]]},"reference":[{"key":"ref_1","unstructured":"Minsky, M., and Papert, S. 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