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PLASMATIC: Exploratory Data Analysis results report

Authors :
ITI
AIMPLAS
Publication Year :
2018
Publisher :
Zenodo, 2018.

Abstract

PLASMATIC (Advanced Predictive Maintenance for the Valencian plastic industrial sector) is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). The general objective of this project is to help the Valencian plastic sector companies to incorporate solutions from the so-called Factory 4.0, via knowledge and technologies in the fields of Big Data, Machine Learning and Business Intelligence. The main result will be an advanced predictive maintenance system to deal with: (i) anomalies detection; (ii) wear prediction; and (iii) maintenance planning optimization. This report shows the results derived from the Exploratory Data Analysis of several benchmarks available in the literature, as well as data from tests carried out by AIMPLAS in one of their injection machines. A methodology for the implementation of an Advanced Predictive Maintenance System (SMPa) is proposed, using the statistical technique Principal Component Analysis. Based on operational and sensor data, PCA computes two statistics that can monitor equipment degradation, detecting anomalies through control charts with enough time for decision making. In addition, contribution plots are able to show what is the cause of such anomaly, which is especially useful in complex systems, because it eases inspection tasks.<br />PLASMATIC. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of the Valencian Community for the development of non-economic R&D projects carried out in cooperation with companies during 2017 with 87.210,96€. File number: IMDEEA/2017/114

Details

Language :
Spanish; Castilian
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....0d3d864fa0064aeba96ae081987bd81f
Full Text :
https://doi.org/10.5281/zenodo.1456666