A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case

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dc.contributor.authorVallim Filho A.R.A.
dc.contributor.authorFarina Moraes D.
dc.contributor.authorBhering de Aguiar Vallim M.V.
dc.contributor.authorda Silva L.S.
dc.contributor.authorda Silva L.A.
dc.date.accessioned2024-03-12T19:15:05Z
dc.date.available2024-03-12T19:15:05Z
dc.date.issued2022
dc.description.abstract© 2022 by the authors. Licensee MDPI, Basel, Switzerland.From a practical point of view, a turbine load cycle (TLC) is defined as the time a turbine in a power plant remains in operation. TLC is used by many electric power plants as a stop indicator for turbine maintenance. In traditional operations, a maximum time for the operation of a turbine is usually estimated and, based on the TLC, the remaining operating time until the equipment is subjected to new maintenance is determined. Today, however, a better process is possible, as there are many turbines with sensors that carry out the telemetry of the operation, and machine learning (ML) models can use this data to support decision making, predicting the optimal time for equipment to stop, from the actual need for maintenance. This is predictive maintenance, and it is widely used in Industry 4.0 contexts. However, knowing which data must be collected by the sensors (the variables), and their impact on the training of an ML algorithm, is a challenge to be explored on a case-by-case basis. In this work, we propose a framework for mapping sensors related to a turbine in a hydroelectric power plant and the selection of variables involved in the load cycle to: (i) investigate whether the data allow identification of the future moment of maintenance, which is done by exploring and comparing four ML algorithms; (ii) discover which are the most important variables (MIV) for each algorithm in predicting the need for maintenance in a given time horizon; (iii) combine the MIV of each algorithm through weighting criteria, identifying the most relevant variables of the studied data set; (iv) develop a methodology to label the data in such a way that the problem of forecasting a future need for maintenance becomes a problem of binary classification (need for maintenance: yes or no) in a time horizon. The resulting framework was applied to a real problem, and the results obtained pointed to rates of maintenance identification with very high accuracies, in the order of 98%.
dc.description.issuenumber10
dc.description.volume15
dc.identifier.doi10.3390/en15103724
dc.identifier.issn1996-1073
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34369
dc.relation.ispartofEnergies
dc.rightsAcesso Aberto
dc.subject.otherlanguageartificial intelligence
dc.subject.otherlanguagebig data process
dc.subject.otherlanguagemachine learning
dc.subject.otherlanguagemost important variables
dc.subject.otherlanguagepredictive maintenance
dc.titleA Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case
dc.typeArtigo
local.scopus.citations14
local.scopus.eid2-s2.0-85130751154
local.scopus.subjectBig data process
local.scopus.subjectEquipment loads
local.scopus.subjectLoad cycle
local.scopus.subjectMachine learning algorithms
local.scopus.subjectMachine learning models
local.scopus.subjectModelling framework
local.scopus.subjectMost important variable
local.scopus.subjectPredictive maintenance
local.scopus.subjectTime horizons
local.scopus.subjectTurbine loads
local.scopus.updated2024-12-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85130751154&origin=inward
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