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Maintenance Decision Support Procedures Based on Machine Learning


Maintenance Decision Support Procedures Based on Machine Learning

Salehabadi, Nooshin (2021) Maintenance Decision Support Procedures Based on Machine Learning. Masters thesis, Concordia University.

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In a competitive production environment, a manufacturing company must have plans to improve production performance. To improve production performances depends on various issues such as production efficiency and machine availability. Various preventive maintenance procedures have been developed for efficiently maintaining and repairing machines and equipment in a manufacturing system to maximize machine availability and its readiness for production. In recent years, Artificial Intelligence (AI) technology has been applied in developing maintenance procedures in industries utilizing advanced information technology such as the Internet of Things (IoT). This thesis presents a machine learning model to predict machine failures and maintenance requirements for certain industrial machine tools. Machine learning methods enable manufacturing systems to make smart decisions through communications with humans and machines using sensors. A Logistic regression model is developed in this study to predict machine failures for the purposes of avoiding machine breakdowns and improving system performance. The supervised classification method was incorporated in the developed prediction model. The developed model is tested verified with realistic machine maintenance data. Computational experiments are conducted with results analyzed.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (Masters)
Authors:Salehabadi, Nooshin
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Industrial Engineering
Date:12 November 2021
Thesis Supervisor(s):Dr. Chen, Mingyuan
ID Code:990075
Deposited By: Nooshin Salehabadi
Deposited On:16 Jun 2022 15:09
Last Modified:16 Jun 2022 15:09
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