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Artificial Intelligence Applied to the Implementation of a Productive Reliability Framework
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The use of Artificial Intelligence (AI) into productive reliability frameworks is transforming industrial and manufacturing sectors. This article describes how maintenance strategies are enhanced, equipment failures can be predicted and overall system performance is addressed by AI. In turning maintenance from a reactive approach to a more preventive one, AI minimizes the time lost, lengthens the life of equipment, and improves economy. Some issues discussed encompass data quality, system integration and organisational adaptability of the workforce in the context of machinery as well as operations and data analyses carried out with the help of AI. Concentrating on current AI interventions and their utility, the article underlines the opportunity born of reliability?s reimagining by an AI-instigated benchmark to drive efficiency, reliability, and sustainability.
Keywords
Artificial Intelligence, productive reliability framework, predictive maintenance, machine learning, industrial operations, data quality, operational resilience, Industry 4.0, workforce adaptation.
How to cite this paper
@article{1706562,
author = {Caio Julio de Souza Oliveira},
title = {Artificial Intelligence Applied to the Implementation of a Productive Reliability Framework},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {5},
pages = {569-579},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706562.pdf},
abstract = {The use of Artificial Intelligence (AI) into productive reliability frameworks is transforming industrial and manufacturing sectors. This article describes how maintenance strategies are enhanced, equipment failures can be predicted and overall system performance is addressed by AI. In turning maintenance from a reactive approach to a more preventive one, AI minimizes the time lost, lengthens the life of equipment, and improves economy. Some issues discussed encompass data quality, system integration and organisational adaptability of the workforce in the context of machinery as well as operations and data analyses carried out with the help of AI. Concentrating on current AI interventions and their utility, the article underlines the opportunity born of reliability?s reimagining by an AI-instigated benchmark to drive efficiency, reliability, and sustainability.},
keywords = {Artificial Intelligence, productive reliability framework, predictive maintenance, machine learning, industrial operations, data quality, operational resilience, Industry 4.0, workforce adaptation.},
month = {November},
}