{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:08:41Z","timestamp":1783570121650,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["2020R1A4A4079904"],"award-info":[{"award-number":["2020R1A4A4079904"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["P0017307"],"award-info":[{"award-number":["P0017307"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"MOTIE (Ministry of Trade, Industry, and Energy)","doi-asserted-by":"publisher","award":["2020R1A4A4079904"],"award-info":[{"award-number":["2020R1A4A4079904"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"MOTIE (Ministry of Trade, Industry, and Energy)","doi-asserted-by":"publisher","award":["P0017307"],"award-info":[{"award-number":["P0017307"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In safety-critical systems such as industrial plants or aircraft, failure occurs inevitably during operation, and it is important to prevent it in order to maintain high availability. To reduce this risk, a lot of efforts are directed from developing sensing technologies to failure prognosis algorithms to enable predictive maintenance. The success of effective and reliable predictive maintenance not only relies on robust prognosis algorithms but also on the selection of sensors or data acquisition strategy. However, there are not many in-depth studies on a trade-off between sensor quality and data storage in the view of prognosis performance. The information about (1) how often data should be measured and (2) how good sensor quality should be for reliable failure prediction can be highly impactful for practitioners. In this paper, the authors evaluate the efficacy of the two factors in terms of remaining useful life (RUL) prediction accuracy and its uncertainty. In addition, since knowing true degradation information is almost impossible in practice, the authors validated the use of the prognosis metric without requiring the true degradation information. A numerical case study is conducted to identify the relationship between sensor quality and data storage. Then, real bearing run-to-failure (RTF) datasets acquired from accelerometer (contact type) and microphone (non-contact type) sensors are evaluated based on the prognosis performance metric and compared in terms of the sensors\u2019 cost-effectiveness for predictive maintenance.<\/jats:p>","DOI":"10.3390\/s22197220","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T03:34:17Z","timestamp":1664163257000},"page":"7220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Trade-Off Analysis between Sensor Quality and Data Intervals for Prognostics Performance"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3174-2392","authenticated-orcid":false,"given":"Hyung Jun","family":"Park","sequence":"first","affiliation":[{"name":"Department of Smart Drone Convergence Engineering, Korea Aerospace University, Goyang-si 10540, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0221-9749","authenticated-orcid":false,"given":"Nam Ho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1709-4491","authenticated-orcid":false,"given":"Joo-Ho","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-si 10540, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.ymssp.2017.11.016","article-title":"Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction","volume":"104","author":"Lei","year":"2018","journal-title":"Mech. 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