Cloud Computing for Industrial Predictive Maintenance Based on Prognostics and Health Management. Fila, R., Khaili, M. E., & Mestari, M. Procedia Computer Science, 177:631–638, January, 2020. ![Cloud Computing for Industrial Predictive Maintenance Based on Prognostics and Health Management [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Predictive maintenance is based primarily on Prognostics and Health Management (PHM). The prognosis is a process for learning about the health status of a system and estimating its residual time before failure. A good maintenance decision is the result of a better estimate of the latter. Recently, the emergence of IT systems in the industrial field and in particular connected objects and cloud computing have contributed strongly to the improvement of the prognosis process. In this paper, we propose a new prognosis approach based on the Cloud Computing model and the principle of multitenancy in order to present the Prognosis as a Service. This approach provides an effective prognosis solution at the request of a client while ensuring a better quality of service. The effectiveness of our solution depends on the criteria for the performance of the prognosis system based on accuracy, accuracy, mean squared error and a Quality of Service (Qos).
@article{fila_cloud_2020,
series = {The 11th {International} {Conference} on {Emerging} {Ubiquitous} {Systems} and {Pervasive} {Networks} ({EUSPN} 2020) / {The} 10th {International} {Conference} on {Current} and {Future} {Trends} of {Information} and {Communication} {Technologies} in {Healthcare} ({ICTH} 2020) / {Affiliated} {Workshops}},
title = {Cloud {Computing} for {Industrial} {Predictive} {Maintenance} {Based} on {Prognostics} and {Health} {Management}},
volume = {177},
issn = {1877-0509},
url = {http://www.sciencedirect.com/science/article/pii/S1877050920323619},
doi = {10.1016/j.procs.2020.10.090},
abstract = {Predictive maintenance is based primarily on Prognostics and Health Management (PHM). The prognosis is a process for learning about the health status of a system and estimating its residual time before failure. A good maintenance decision is the result of a better estimate of the latter. Recently, the emergence of IT systems in the industrial field and in particular connected objects and cloud computing have contributed strongly to the improvement of the prognosis process. In this paper, we propose a new prognosis approach based on the Cloud Computing model and the principle of multitenancy in order to present the Prognosis as a Service. This approach provides an effective prognosis solution at the request of a client while ensuring a better quality of service. The effectiveness of our solution depends on the criteria for the performance of the prognosis system based on accuracy, accuracy, mean squared error and a Quality of Service (Qos).},
language = {en},
urldate = {2020-11-16},
journal = {Procedia Computer Science},
author = {Fila, Redouane and Khaili, Mohamed El and Mestari, Mohamed},
month = jan,
year = {2020},
keywords = {Cloud Computing, Health Management (PHM), Performance Measurement, Predictive Maintenance, Prognosis as a Service, Prognostics, Quality of Service (Qos), Residual Life (RUL)},
pages = {631--638},
}
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