Advanced Analytics in Smart Manufacturing: Anomaly Detection Using Machine Learning Algorithms and Parallel Machine Scheduling Using a Genetic Algorithm

Author

Meiling He, University of Wisconsin-Milwaukee

Date of Award

December 2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Engineering

First Advisor

Matthew Petering

Committee Members

Jun Zhang, Zeyun Yu, Francisco Maturana, Chiu Tai Law

Keywords

Anomaly Detection, Auto ML, Machine Learning, Scheduling Optimization, Smart Manufacturing

Abstract

Industry 4.0 offers great opportunities to utilize advanced data processing tools by generating Big Data from a more connected and efficient data collection system. Making good use of data processing technologies, such as machine learning and optimization algorithms, will significantly contribute to better quality control, automation, and job scheduling in Smart Manufacturing. This research aims to develop a new machine learning algorithm for solving highly imbalanced data processing problems, implement both supervised and unsupervised machine learning auto-selection frameworks for detecting anomalies in smart manufacturing, and develop a genetic algorithm for optimizing job schedules on unrelated parallel machines. This research also demonstrates the case studies and model analyses to validate the effectiveness and applications of the above-mentioned algorithms and frameworks for solving proposed problems.

Recommended Citation

He, Meiling, "Advanced Analytics in Smart Manufacturing: Anomaly Detection Using Machine Learning Algorithms and Parallel Machine Scheduling Using a Genetic Algorithm" (2021). Theses and Dissertations. 2790.
https://dc.uwm.edu/etd/2790