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Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management
Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prog...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537368/ https://www.ncbi.nlm.nih.gov/pubmed/34696058 http://dx.doi.org/10.3390/s21206841 |
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author | Cofre-Martel, Sergio Lopez Droguett, Enrique Modarres, Mohammad |
author_facet | Cofre-Martel, Sergio Lopez Droguett, Enrique Modarres, Mohammad |
author_sort | Cofre-Martel, Sergio |
collection | PubMed |
description | Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers. |
format | Online Article Text |
id | pubmed-8537368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85373682021-10-24 Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management Cofre-Martel, Sergio Lopez Droguett, Enrique Modarres, Mohammad Sensors (Basel) Article Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers. MDPI 2021-10-14 /pmc/articles/PMC8537368/ /pubmed/34696058 http://dx.doi.org/10.3390/s21206841 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cofre-Martel, Sergio Lopez Droguett, Enrique Modarres, Mohammad Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management |
title | Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management |
title_full | Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management |
title_fullStr | Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management |
title_full_unstemmed | Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management |
title_short | Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management |
title_sort | big machinery data preprocessing methodology for data-driven models in prognostics and health management |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537368/ https://www.ncbi.nlm.nih.gov/pubmed/34696058 http://dx.doi.org/10.3390/s21206841 |
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