@article{nguyen_new_2019,
	title = {A new dynamic predictive maintenance framework using deep learning for failure prognostics},
	volume = {188},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832018311050},
	doi = {10.1016/j.ress.2019.03.018},
	abstract = {In Prognostic Health and Management (PHM) literature, the predictive maintenance studies can be classified into two groups. The first group focuses on the prognostics step but does not consider the maintenance decisions. The second group addresses the maintenance optimization question based on the assumptions that the prognostics information or the degradation models of the system are already known. However, none of the two groups provides a complete framework (from data-driven prognostics to maintenance decisions) investigating the impact of the imperfect prognostics on maintenance decision. Therefore, this paper aims to fill this gap of literature. It presents a novel dynamic predicive maintenance framework based on sensor measurements. In this framework, the prognostics step, based on the Long Short-Term Memory network, is oriented towards the requirements of operation planners. It provides the probabilities that the system can fail in different time horizons to decide the moment for preparing and performing maintenance activities. The proposed framework is validated on a real application case study. Its performance is highlighted when compared with two benchmark maintenance policies: classical periodic and ideal predicted maintenance. In addition, the impact of the imperfect prognostics information on maintenance decisions is discussed in this paper.},
	language = {en},
	urldate = {2021-09-28},
	journal = {Reliability Engineering \& System Safety},
	author = {Nguyen, Khanh T. P. and Medjaher, Kamal},
	month = aug,
	year = {2019},
	keywords = {Deep learning, Inventory management, PHM, Predictive maintenance, Prognostics information, Residual life prediction},
	pages = {251--262},
}

{"_id":"4EZrooag6by9tct5W","bibbaseid":"nguyen-medjaher-anewdynamicpredictivemaintenanceframeworkusingdeeplearningforfailureprognostics-2019","author_short":["Nguyen, K. T. P.","Medjaher, K."],"bibdata":{"bibtype":"article","type":"article","title":"A new dynamic predictive maintenance framework using deep learning for failure prognostics","volume":"188","issn":"0951-8320","url":"https://www.sciencedirect.com/science/article/pii/S0951832018311050","doi":"10.1016/j.ress.2019.03.018","abstract":"In Prognostic Health and Management (PHM) literature, the predictive maintenance studies can be classified into two groups. The first group focuses on the prognostics step but does not consider the maintenance decisions. The second group addresses the maintenance optimization question based on the assumptions that the prognostics information or the degradation models of the system are already known. However, none of the two groups provides a complete framework (from data-driven prognostics to maintenance decisions) investigating the impact of the imperfect prognostics on maintenance decision. Therefore, this paper aims to fill this gap of literature. It presents a novel dynamic predicive maintenance framework based on sensor measurements. In this framework, the prognostics step, based on the Long Short-Term Memory network, is oriented towards the requirements of operation planners. It provides the probabilities that the system can fail in different time horizons to decide the moment for preparing and performing maintenance activities. The proposed framework is validated on a real application case study. Its performance is highlighted when compared with two benchmark maintenance policies: classical periodic and ideal predicted maintenance. In addition, the impact of the imperfect prognostics information on maintenance decisions is discussed in this paper.","language":"en","urldate":"2021-09-28","journal":"Reliability Engineering & System Safety","author":[{"propositions":[],"lastnames":["Nguyen"],"firstnames":["Khanh","T.","P."],"suffixes":[]},{"propositions":[],"lastnames":["Medjaher"],"firstnames":["Kamal"],"suffixes":[]}],"month":"August","year":"2019","keywords":"Deep learning, Inventory management, PHM, Predictive maintenance, Prognostics information, Residual life prediction","pages":"251–262","bibtex":"@article{nguyen_new_2019,\n\ttitle = {A new dynamic predictive maintenance framework using deep learning for failure prognostics},\n\tvolume = {188},\n\tissn = {0951-8320},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0951832018311050},\n\tdoi = {10.1016/j.ress.2019.03.018},\n\tabstract = {In Prognostic Health and Management (PHM) literature, the predictive maintenance studies can be classified into two groups. The first group focuses on the prognostics step but does not consider the maintenance decisions. The second group addresses the maintenance optimization question based on the assumptions that the prognostics information or the degradation models of the system are already known. However, none of the two groups provides a complete framework (from data-driven prognostics to maintenance decisions) investigating the impact of the imperfect prognostics on maintenance decision. Therefore, this paper aims to fill this gap of literature. It presents a novel dynamic predicive maintenance framework based on sensor measurements. In this framework, the prognostics step, based on the Long Short-Term Memory network, is oriented towards the requirements of operation planners. It provides the probabilities that the system can fail in different time horizons to decide the moment for preparing and performing maintenance activities. The proposed framework is validated on a real application case study. Its performance is highlighted when compared with two benchmark maintenance policies: classical periodic and ideal predicted maintenance. In addition, the impact of the imperfect prognostics information on maintenance decisions is discussed in this paper.},\n\tlanguage = {en},\n\turldate = {2021-09-28},\n\tjournal = {Reliability Engineering \\& System Safety},\n\tauthor = {Nguyen, Khanh T. P. and Medjaher, Kamal},\n\tmonth = aug,\n\tyear = {2019},\n\tkeywords = {Deep learning, Inventory management, PHM, Predictive maintenance, Prognostics information, Residual life prediction},\n\tpages = {251--262},\n}\n\n","author_short":["Nguyen, K. T. P.","Medjaher, K."],"key":"nguyen_new_2019","id":"nguyen_new_2019","bibbaseid":"nguyen-medjaher-anewdynamicpredictivemaintenanceframeworkusingdeeplearningforfailureprognostics-2019","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0951832018311050"},"keyword":["Deep learning","Inventory management","PHM","Predictive maintenance","Prognostics information","Residual life prediction"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["deep learning","inventory management","phm","predictive maintenance","prognostics information","residual life prediction"],"search_terms":["new","dynamic","predictive","maintenance","framework","using","deep","learning","failure","prognostics","nguyen","medjaher"],"title":"A new dynamic predictive maintenance framework using deep learning for failure prognostics","year":2019}