@article{nunez_ontoprog_2018,
	title = {{OntoProg}: {An} ontology-based model for implementing {Prognostics} {Health} {Management} in mechanical machines},
	volume = {38},
	issn = {1474-0346},
	shorttitle = {{OntoProg}},
	url = {https://www.sciencedirect.com/science/article/pii/S1474034617306080},
	doi = {10.1016/j.aei.2018.10.006},
	abstract = {Trends in Prognostics Health Management (PHM) have been introduced into mechanical items of manufacturing systems to predict Remaining Useful Life (RUL). PHM as an estimate of the RUL allows Condition-based Maintenance (CBM) before a functional failure occurs, avoiding corrective maintenance that generates unnecessary costs on production lines. An important factor for the implementation of PHM is the correct data collection for monitoring a machine’s health, in order to evaluate its reliability. Data collection, besides providing information about the state of degradation of the machine, also assists in the analysis of failures for intelligent interventions. Thus, the present work proposes the construction of an ontological model for future applications such as expert system in the support in the correct decision-making, besides assisting in the implementation of the PHM in several manufacturing scenarios, to be used in the future by web semantics tools focused on intelligent manufacturing, standardizing its concepts, terms, and the form of collection and processing of data. The methodological approach Design Science Research (DSR) is used to guide the development of this study. The model construction is achieved using the ontology development 101 procedure. The main result is the creation of the ontological model called OntoProg, which presents: a generic ontology addressing by international standards, capable of being used in several types of mechanical machines, of different types of manufacturing, the possibility of storing the knowledge contained in events of real activities that allow through consultations in SPARQL for decision-making which enable timely interventions of maintenance in the equipment of a real industry. The limitation of the work is that said model can be implemented only by specialists who have knowledge in ontology.},
	language = {en},
	urldate = {2021-09-28},
	journal = {Advanced Engineering Informatics},
	author = {Nuñez, David Lira and Borsato, Milton},
	month = oct,
	year = {2018},
	keywords = {Failure analysis, Ontology engineering, Prognostics Health Management},
	pages = {746--759},
}

{"_id":"g7ny5XPNzuAg9wCnu","bibbaseid":"nuez-borsato-ontoproganontologybasedmodelforimplementingprognosticshealthmanagementinmechanicalmachines-2018","author_short":["Nuñez, D. L.","Borsato, M."],"bibdata":{"bibtype":"article","type":"article","title":"OntoProg: An ontology-based model for implementing Prognostics Health Management in mechanical machines","volume":"38","issn":"1474-0346","shorttitle":"OntoProg","url":"https://www.sciencedirect.com/science/article/pii/S1474034617306080","doi":"10.1016/j.aei.2018.10.006","abstract":"Trends in Prognostics Health Management (PHM) have been introduced into mechanical items of manufacturing systems to predict Remaining Useful Life (RUL). PHM as an estimate of the RUL allows Condition-based Maintenance (CBM) before a functional failure occurs, avoiding corrective maintenance that generates unnecessary costs on production lines. An important factor for the implementation of PHM is the correct data collection for monitoring a machine’s health, in order to evaluate its reliability. Data collection, besides providing information about the state of degradation of the machine, also assists in the analysis of failures for intelligent interventions. Thus, the present work proposes the construction of an ontological model for future applications such as expert system in the support in the correct decision-making, besides assisting in the implementation of the PHM in several manufacturing scenarios, to be used in the future by web semantics tools focused on intelligent manufacturing, standardizing its concepts, terms, and the form of collection and processing of data. The methodological approach Design Science Research (DSR) is used to guide the development of this study. The model construction is achieved using the ontology development 101 procedure. The main result is the creation of the ontological model called OntoProg, which presents: a generic ontology addressing by international standards, capable of being used in several types of mechanical machines, of different types of manufacturing, the possibility of storing the knowledge contained in events of real activities that allow through consultations in SPARQL for decision-making which enable timely interventions of maintenance in the equipment of a real industry. The limitation of the work is that said model can be implemented only by specialists who have knowledge in ontology.","language":"en","urldate":"2021-09-28","journal":"Advanced Engineering Informatics","author":[{"propositions":[],"lastnames":["Nuñez"],"firstnames":["David","Lira"],"suffixes":[]},{"propositions":[],"lastnames":["Borsato"],"firstnames":["Milton"],"suffixes":[]}],"month":"October","year":"2018","keywords":"Failure analysis, Ontology engineering, Prognostics Health Management","pages":"746–759","bibtex":"@article{nunez_ontoprog_2018,\n\ttitle = {{OntoProg}: {An} ontology-based model for implementing {Prognostics} {Health} {Management} in mechanical machines},\n\tvolume = {38},\n\tissn = {1474-0346},\n\tshorttitle = {{OntoProg}},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1474034617306080},\n\tdoi = {10.1016/j.aei.2018.10.006},\n\tabstract = {Trends in Prognostics Health Management (PHM) have been introduced into mechanical items of manufacturing systems to predict Remaining Useful Life (RUL). PHM as an estimate of the RUL allows Condition-based Maintenance (CBM) before a functional failure occurs, avoiding corrective maintenance that generates unnecessary costs on production lines. An important factor for the implementation of PHM is the correct data collection for monitoring a machine’s health, in order to evaluate its reliability. Data collection, besides providing information about the state of degradation of the machine, also assists in the analysis of failures for intelligent interventions. Thus, the present work proposes the construction of an ontological model for future applications such as expert system in the support in the correct decision-making, besides assisting in the implementation of the PHM in several manufacturing scenarios, to be used in the future by web semantics tools focused on intelligent manufacturing, standardizing its concepts, terms, and the form of collection and processing of data. The methodological approach Design Science Research (DSR) is used to guide the development of this study. The model construction is achieved using the ontology development 101 procedure. The main result is the creation of the ontological model called OntoProg, which presents: a generic ontology addressing by international standards, capable of being used in several types of mechanical machines, of different types of manufacturing, the possibility of storing the knowledge contained in events of real activities that allow through consultations in SPARQL for decision-making which enable timely interventions of maintenance in the equipment of a real industry. 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