Authors:
Hasan Moradizadeh
and
Rene V. Mayorga
Affiliation:
University of Regina, Canada
Keyword(s):
Intelligent Systems, Fuzzy Inference Systems, Overall Equipment Effectiveness, Overall Line Efficiency, Six Major Losses in Industry.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Fuzzy Systems Design, Modeling and Control
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Increasingly, Intelligent Systems (IS) techniques are being used to solve both complex problems and industrial problems with uncertainty. They also can implement the operator’s knowledge (experience) into the system. This Paper aims to improve and compute the well-known manufacturing metrics: the Overall Equipment Effectiveness (OEE), and Overall Line Efficiency (OLE), using IS techniques. The proposed methodologies to improve the OEE and OLE weakness are based on Fuzzy Inference Systems. These techniques result in an effective way to measure OEE and OLE considering different weight of losses and also the difference in machine’s weight factors. Moreover, they allow the operator’s knowledge to be taken into account in the measurement using uncertain input and output with implementation of linguistic terms.