2020
DOI: 10.18502/keg.v5i6.7105
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Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications

Abstract: Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance  and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenanc… Show more

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Cited by 7 publications
(4 citation statements)
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“…According to Nacchia et al (2021) [20], PdM has shown great potential when guided by an ML algorithm. Several studies have corroborated this statement, such as those by Zhang and Yang (2019) [18]; Chen et al (2020a) [19]; Nentwich, Junker, and Reinhart (2020) [21]; Martins, Rodriguez, and Henriques (2020) [22]; Welte, Estler, and Lucke (2020) [23]; and Theissler et al (2021) [17]. Thus, the next section presents the relevant fundamentals of ML for PdM applications.…”
mentioning
confidence: 76%
“…According to Nacchia et al (2021) [20], PdM has shown great potential when guided by an ML algorithm. Several studies have corroborated this statement, such as those by Zhang and Yang (2019) [18]; Chen et al (2020a) [19]; Nentwich, Junker, and Reinhart (2020) [21]; Martins, Rodriguez, and Henriques (2020) [22]; Welte, Estler, and Lucke (2020) [23]; and Theissler et al (2021) [17]. Thus, the next section presents the relevant fundamentals of ML for PdM applications.…”
mentioning
confidence: 76%
“…Additionally, big data platforms are employed for real-time predictive assessment of oscillatory stability, utilizing techniques like recurrent neural networks and leveraging records from tools like WAProtector [13]. The effective functioning of smart electricity grids hinges on acquiring, analyzing, and processing the enormous volume of data generated by various components, including smart sensors, individual smart meters, and environmental data collection devices like solar radiation sensors and wind-speed meters [14]. Addressing the challenges posed by the sheer scale of this data necessitates the adoption of cutting-edge data analytics, comprehensive big data management strategies, and robust monitoring methodologies.…”
Section: Big Data Analytics In Electric Grid Systemsmentioning
confidence: 99%
“…A case study was presented as an example, showcasing a predictive model capable of anticipating unexpected machine failures. Martins et al [9], implemented an automatic forecasting model in a test bench to identify machine failures and contribute to the advancement of algorithms for preventive and descriptive maintenance. This implementation aims to improve the recognition of machine failures and facilitate the development of maintenance strategies that are both preventive and descriptive in nature.…”
Section: Related Workmentioning
confidence: 99%