PHM Fundamentals (cancelled)
We welcome the interest of attendees in registering for short courses to prepare for the conference and to advance their professional development.
Unfortunately there were inadequate registrations to run the Fundamentals course (same days, same place, same cost, almost as much fun).
We are encouraging current registrants and others considering a short course to proceed to sign up for the Analytics short course. While emphasizing data analytics and machine learning methods and examples, the instructors approach and enthusiasm and examples will be of broad interest. There will be additional instructors there to help participants.
Analytics for PHM – Advanced Course
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 the fundamentals course or contact the course leader to examine their background and skills.
The course is about two thirds lecture, and an optional one third hands-on lab. Students who elect to take the lab will be ex 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.
Course leader: Dr. Neil Eklund
Topics included:
• Overview of data-driven PHM
• Review of Fundamental statistics
• Data Visualization
• Machine learning – introduction and concepts
• Data transformation & feature extraction
• Classification
• Regression
• Introduction to Neural Networks
• Hands-on Lab
• Feature selection
• Characterizing performance
• Model Selection
• Anomaly detection
• Deep Learning I
• Deep Learning II
• Applications
• Practical matters
… Bottom Line: Education is important to The PHM Society Education
Taking a course first is an excellent preparation to benefit from the conference and to meet students, professors and industry people from around the world and across diverse sectors.
Come for the entire conference and get reduced tuition. Special rates for fulltime students.
The educational and training experience is even better at the conference- free tutorials, workshops, peer-reviewed technical papers, poster sessions, technology demonstrations, doctoral symposium, data challenge and panel sessions. And the social events are planned to help make networking easy and enjoyable.
For information contact shortcourse@phmconference.org.