Analytics for PHM Short Course
Monday and Tuesday, 2-3 July 2018
Overview
This course is intended for engineers, scientists, and managers who are interested in data driven methods for asset health management. You will learn how to identify potential data driven projects, visualize data, screen data, construct and select appropriate features, build models of assets from data, evaluate and select models, and deploy asset monitoring systems.
By the end of the course, you will have learned the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, detecting anomalous behavior, diagnosing faults, and estimating remaining useful life.
Note that this course is an advanced course with only a brief, high-level overview of PHM presented – students are expected to know the basics of PHM already. New practitioners are encouraged to take fundamentals course or contact the course leader to examine their background and skills.
Delivery Method
The course is about two thirds lecture, and an optional one third hands-on lab. Students who elect to take the lab will be expected to bring a laptop with analytics software (R, Python, Matlab, or something similar) that they are familiar with pre-installed. Lab example solutions will be presented in Python.
Duration
Two, eight-hour days.
Course Credit
A PHM Society Certificate will be provided for nominally 1.4 Continuing Professional Development Units to each participant completing the lecture and lab portion of the course, or 1.0 Continuing Professional Development Units to each participant completing only the lecture.
Who Should Attend
The course is designed for two primary types of students:
- Managers who oversee asset health management projects, and want to know more about the technical details behind the process.
- Practitioners who want to know the theory and get hands-on experience for data driven PHM, including:
- Students
- New engineers and scientists
- Experienced engineers and scientists looking to update their skills and understanding data driven methods
- Project managers who incorporate data driven PHM in their projects
- Individuals with a general understanding of analytics who want to see how it is applied to PHM
Lecturer: Dr. Neil Eklund
Administrator: Jeff Bird jeffbird@rogers.com
Location: Muntgebouw Conference Center, Utrecht, The Netherlands
Tuition: To be confirmed in the spring. Contact course administrator for more information.
Course Schedule:
1230 – 130Lunch (provided with evaluation forms)
| DAY 1 | Topic |
|---|---|
| 800 – 1030 | Session 1: |
| Welcome and Introduction | |
| Overview of data-driven PHM | |
| Review of fundamental statistics | |
| Data visualization | |
| 1030-1045 | Break |
| 1045-1200 | Session 2: |
| Machine learning – introduction and concepts | |
| Data transformation & feature extraction | |
| Classification | |
| 1200 – 100 | Lunch (provided) |
| 100 – 315 | Session 3: |
| Regression | |
| Introduction to Neural Networks | |
| 315 – 330 | Break |
| 330-515 | Session 4: |
| Hands-on lab | |
| DAY 2 | Topic |
| 830 – 1030 | Session 5: |
| Feature selection | |
| Characterizing performance | |
| Model selection | |
| Anomaly detection | |
| 1030-1045 | Break |
| 1045-1230 | Session 6: |
| Deep learning | |
| 130 – 320 | Session 7: |
| Regression | |
| Applications | |
| Practical matters | |
| 320 – 340 | Break |
| 340 – 515 | Session 8: |
| Hands-on lab | |
| Wrap up with evaluation forms |