Introduction of Machine Learning Techniques for Reliability Data Analysis
Sunday January 26
This full-day course is designed for reliability engineers and professionals looking to enhance their skills with cutting-edge machine learning and artificial intelligence tools. The course will cover key machine learning concepts and focus on two main techniques: tree-based methods (including decision trees and random forests) and neural network-based methods (such as CNNs, RNNs, and LSTMs). Attendees will learn how to apply these techniques using no-code tools like Tabula, making it accessible even for those without formal programming training.
Prerequisites for this course include a basic understanding of statistics, probability, and reliability engineering concepts, as well as basic data analysis skills, such as those used in Excel. By the end of the course, participants will be able to preprocess and analyze reliability data, build and evaluate machine learning models, and understand the potential applications of recent AI technologies, including Large Language Models (LLMs), in reliability data analysis.
More info and registration: https://rams.org/2025-rams-course-ml-for-reliability-data/
Course Content
| Time | Topic | Description |
| 8:00 AM – 8:30 AM | Registration and Introduction | Welcome, course overview, introduction to machine learning and its relevance to reliability engineering. |
| 8:30 AM – 9:30 AM | Fundamentals of Machine Learning | Definitions, types of machine learning, key concepts, overview of no-code machine learning tools. |
| 9:30 AM – 10:30 AM | Data Preprocessing and Exploration | Importing and cleaning reliability data in Tabula, handling missing values, EDA techniques, visualization. |
| 10:30 AM – 10:45 AM | Break | |
| 10:45 AM – 12:00 PM | Traditional Reliability Data Analysis Methods | Survival analysis, reliability function, hazard function, parametric models, non-parametric methods. |
| 12:00 PM – 1:00 PM | Lunch Break | |
| 1:00 PM – 2:30 PM | Tree-Based Methods for Reliability Analysis | Introduction to decision trees, building and interpreting random forests, hands-on exercises using Tabula. |
| 2:30 PM – 2:45 PM | Break | |
| 2:45 PM – 4:15 PM | Neural Network-Based Methods for Reliability | Basics of neural networks, CNNs, RNNs, LSTMs, hands-on exercises using Tabula. |
| 4:15 PM – 5:00 PM | Introduction to Recent AI Technologies | Overview of LLMs, applications of LLMs in reliability data analysis, demonstrations. |
By the end of the course, attendees will have a solid understanding of how various machine learning techniques can be applied to reliability data analysis using no-code tools like Tabula. They will also gain insights into the potential applications of recent AI technologies, including LLMs, in their field.
Instructor
Dr. Rong Pan is Professor of Industrial Engineering and Data Science in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). He is the Program Chair of Data Science, Analytics, and Engineering (DSAE) program at ASU. His research interests include failure time data analysis, design of experiments, multivariate statistical process control, time series analysis, and computational Bayesian methods. His research has been supported by NSF, Arizona Science Foundation, Air Force Research Lab, etc. He has published over 90 journal papers and 50+ refereed conference papers. Dr. Pan is a senior member of ASQ, IIE, and IEEE, and a lifetime member of SRE. He currently serves as the Chair of ASQ Reliability and Risk Division and the Editor-elect of Journal of Quality Technology.
