Author ORCID Identifier
Semester
Fall
Date of Graduation
2024
Document Type
Dissertation
Degree Type
PhD
College
Statler College of Engineering and Mineral Resources
Department
Industrial and Managements Systems Engineering
Committee Chair
Thorsten Wuest
Committee Member
Zhichao Liu
Committee Member
Imtiaz Ahmed
Committee Member
Todd Hamrick
Committee Member
Ramy Harik
Abstract
We are witnessing a transformation in manufacturing industries driven by integrating Industry 4.0 infrastructures and smart manufacturing systems. This is being made possible by leveraging advanced analytical technologies like AI, Machine Learning (ML), and Digital Twins leading to lower costs, higher efficiency, and quality improvements. Time-series analytics plays a pivotal role in the manufacturing domain due to the abundance of time-series, and it has the potential to be used in different applications such as fault detection, predictive maintenance, energy and demand forecasting, and remaining useful life prediction. Consequently, Time-Series Classification (TSC) and Time-Series Forecasting (TSF) are two main tasks in this domain to set the foundation for predictive applications in smart manufacturing systems.
Over the past decade, researchers have introduced many algorithms for TSC and TSF, necessitating validation, and empirical comparison of them alongside development. This dual approach holds substantial value for researchers and practitioners by streamlining choices and revealing insights into models' strengths and weaknesses. There is a gap in manufacturing where theoretical advancements have not been fully translated into practical applications. The main objective of this Ph.D. dissertation is to develop a comprehensive framework that will help to bridge the gap, provide guidance for practitioners, and advance data analytics in manufacturing systems. This was done by performing a rigorous experimental evaluation of SoTA ML and DL algorithms on a variety of datasets for the mentioned tasks. Additionally, the scarcity of manufacturing datasets was addressed by providing sets of manufacturing-related datasets, preprocessed to facilitate algorithm implementation.
For the TSC framework, 92 algorithms were identified, reviewed, and categorized. These included ML, ANN, and DL algorithms from different feature engineering and classification techniques. Thirty-six most representative algorithms were methodologically selected for evaluation on a set of 22 manufacturing datasets with different characteristics, covering diverse manufacturing problems. For TSF, more than 100 algorithms from Statistical, ML, and DL approaches and 29 datasets were studied. Subsequently, the experimental evaluation was conducted on a subset of 18 representative algorithms and 13 datasets over four different experimental scenarios. Subtopics and use cases for TSC and TSF frameworks were also explored, including high-resolution time-series classification and real-time defect detection in a robotic assembly line using original research data. By conducting extensive evaluations, creating curated datasets, and demonstrating real-world applications, this dissertation aims to contribute to the body of knowledge for more efficient data-driven decision-making in manufacturing industries. The developed frameworks are an effort toward this goal to establish baselines for researchers and provide guidelines for practitioners for time-series analytics tasks.
Recommended Citation
A. Farahani, Mojtab, "A Data-driven Time-series Analytics Framework for Smart Manufacturing Systems" (2024). Graduate Theses, Dissertations, and Problem Reports. 12712.
https://researchrepository.wvu.edu/etd/12712
Comments
the ETD's new version with minor revisions and based on the comments