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Prognosease: A Data Generator for Health Deterioration Prognosis
Version 1
: Received: 25 January 2023 / Approved: 26 January 2023 / Online: 26 January 2023 (08:37:30 CET)
A peer-reviewed article of this Preprint also exists.
Berghout, T.; Benbouzid, M. PrognosEase: A Data Generator for Health Deterioration Prognosis. SoftwareX 2023, 101461, doi:10.1016/j.softx.2023.101461. Berghout, T.; Benbouzid, M. PrognosEase: A Data Generator for Health Deterioration Prognosis. SoftwareX 2023, 101461, doi:10.1016/j.softx.2023.101461.
Abstract
This paper presents PrognosEase; a software that provides an easier way to produce different types of run-to-failure data mimicking real-world conditions to simplify prognosis studies in terms of data collection and improvement in ML degradation modelling process. Different types of degradation types made available to meet different types of applications. Besides, some primary ML tests were performed to ensure that complexity patterns of real systems could be observed in the training/testing predictions attitude. This paper also presents the impacts, limitations and potential improvements of the data generator.
Keywords
Data generator; dataset; deep learning; health index; machine learning; prognosis and health management; remaining useful life
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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