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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...

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Detalles Bibliográficos
Autores principales: Cofre-Martel, Sergio, Lopez Droguett, Enrique, Modarres, Mohammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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