Authors:
Kenan Cem Demirel
;
Ahmet Şahin
and
Erinc Albey
Affiliation:
Department of Industrial Engineering, Özyeğin University, Istanbul, 34794 and Turkey
Keyword(s):
Industry 4.0, Ensemble Methods, Multi-Target Regression, Regression Chains, Quality Prediction, Textile Manufacturing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Management of Sensor Data
;
Predictive Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
In this study we construct a prediction model, which utilizes the production process parameters acquired from a textile machine and predicts the quality characteristics of the final yarn. Several machine learning algorithms (decision tree, multivariate adaptive regression splines and random forest) are used for prediction. An ensemble method, using the idea of regressor chains, is developed to further improve the prediction performance. Collected data is first segmented into two parts (labeled as “normal” and “unusual”) using local outlier factor method, and performance of the algorithms are tested for each segment separately. It is seen that ensemble idea proves its competence especially for the cases where the collected data is categorized as unusual. In such cases ensemble algorithm improves the prediction accuracy significantly.