{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:50:02Z","timestamp":1771307402229,"version":"3.50.1"},"reference-count":50,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"unspecified","delay-in-days":17,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100006999","name":"Nuclear Energy University Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006999","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:title>Abstract<\/jats:title>\n\t  <jats:p>Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.<\/jats:p>","DOI":"10.1017\/s0890060423000215","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T08:36:33Z","timestamp":1705566993000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":4,"title":["Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems"],"prefix":"10.1017","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4672-6352","authenticated-orcid":false,"given":"Xiaoxu","family":"Diao","sequence":"first","affiliation":[]},{"given":"Yunfei","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Pavan K.","family":"Vaddi","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Pietrykowski","sequence":"additional","affiliation":[]},{"given":"Marat","family":"Khafizov","sequence":"additional","affiliation":[]},{"given":"Carol","family":"Smidts","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2024,1,18]]},"reference":[{"key":"S0890060423000215_r5","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0951-8320(99)00068-X","article-title":"Risk based maintenance optimization: Foundational issues","volume":"67","author":"Apeland","year":"2000","journal-title":"Reliability Engineering and System Safety"},{"key":"S0890060423000215_r35","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.artmed.2017.07.002","article-title":"Owlready: Ontology-oriented programming in python with automatic classification and high-level constructs for biomedical ontologies","volume":"80","author":"Lamy","year":"2017","journal-title":"Artificial Intelligence in Medicine"},{"key":"S0890060423000215_r9","first-page":"68","article-title":"An information artifact ontology perspective on data collections and associated representational artifacts","volume":"180","author":"Ceusters","year":"2012","journal-title":"Studies in Health Technology and Informatics"},{"key":"S0890060423000215_r36","doi-asserted-by":"crossref","first-page":"110805","DOI":"10.1115\/1.4047856","article-title":"Intelligent maintenance systems and predictive manufacturing","volume":"142","author":"Lee","year":"2020","journal-title":"Journal of Manufacturing Science and Engineering, Transactions of the ASME"},{"key":"S0890060423000215_r28","unstructured":"INTERNATIONAL ATOMIC ENERGY AGENCY (2018) Maintenance Optimization Programme for Nuclear Power Plants., IAEA Nuclear Energy Series No. NP-T-3.8, IAEA, Vienna."},{"key":"S0890060423000215_r43","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/978-981-10-0356-1_58","article-title":"User oriented semi-automatic method of constructing domain ontology","volume":"575","author":"Qu","year":"2016","journal-title":"Communications in Computer and Information Science"},{"key":"S0890060423000215_r42","unstructured":"Oltramari, A , Henshel, D , Cains, M and Hoffman, B (2015) Towards a human factors ontology for cyber security. 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