PHM Society Conference 2025 Hands-On Workshop: Anomaly Detection and Predictive Maintenance with MATLAB
Overview
Engineers are using AI to design smarter ways to detect anomalies, identify faults, and estimate the remaining useful life of machines. In this hands-on workshop, you will write and execute code examples in MATLAB® Online™ – entirely in your browser – to learn and explore how to design AI algorithms for anomaly detection and predictive maintenance. This workshop will be most useful for engineers who work with time series sensor data that want to explore ways to apply MATLAB to design effective algorithms.
MathWorks instructors will be available throughout the session to guide you. All necessary MATLAB licenses will be provided for the duration of the workshop, including Predictive Maintenance Toolbox. Please bring your laptop.
Please only register for one workshop slot. Both workshops have the same content.
Highlights
- Familiarize yourself with MATLAB Online with an introductory example that trains a machine learning model to identify anomalies and faults.
- Interactively extract, analyze, and rank features from machine sensor data, then train and compare multiple AI models based on these features.
- Dive deep into two real-world examples that cover designing an anomaly detection algorithm and estimating remaining useful life
Who Should Attend
This workshop is open to anyone attending the PHM Society Conference in 2025 who is interested in learning how to design predictive maintenance and anomaly detection algorithms in MATLAB.
About the Presenter
Reece Teramoto is a Senior Application Engineer at MathWorks, where he has spent the past eight years collaborating with engineers and researchers across the Aerospace, Defense, and Government sectors. Reece specializes in artificial intelligence (AI), machine learning, and data analytics, and has partnered with multiple branches of the U.S. Department of Defense—including the Army, Navy, Air Force, and NASA—on AI-driven projects. His work includes supporting the analysis of aircraft wear and tear through predictive maintenance and AI techniques, helping organizations enhance operational readiness and equipment reliability.
In addition to his technical contributions, Reece is an experienced workshop leader, regularly delivering hands-on sessions at conferences and customer sites to empower teams in applying AI and data analytics to real-world challenges. He holds dual degrees in Computer Science and Electrical Engineering from the University of Portland.
Rachel Johnson is the Principal Product Manager for Predictive Maintenance Toolbox at MathWorks, and has been using MATLAB for more than 20 years. Previously, she was an Application Engineer at MathWorks supporting the aerospace industry. Before MathWorks, Rachel worked at the Office of Naval Intelligence in Washington, D.C. where she used MATLAB and Simulink to analyze and simulate missile aerodynamics and propulsion systems. She also spent several years teaching high school math, physics, and engineering. Rachel holds a B.S.E. in Aerospace Engineering from Princeton University and an M.S. in Aerospace Engineering from the University of Maryland. Rachel has attended PHM Society Conferences since 2019.
Bora Eryilmaz is a Principal Software Developer in the Controls and Identification group at MathWorks. He has been with MathWorks since 2000 in various software development and software engineering management roles. As a developer, he has contributed algorithmic and user interface solutions to various toolboxes. He is currently with the Predictive Maintenance Toolbox team, developing new applications for anomaly detection, condition monitoring, and predictive maintenance of engineering systems.
Bora holds a Ph.D. in Mechanical Engineering from Northeastern University, where he also taught graduate and undergraduate courses. His research spanned modeling and control of electro hydraulic systems resulting in numerous journal papers and conference presentations. Bora also holds a number of U.S. and international patents in the areas of modeling, simulation, and controls.
