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:

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
1230 - 130 Lunch (provided with evaluation forms)
130 - 320 Session 7:
  Regression
  Applications
  Practical matters
320 - 340 Break
340 - 515 Session 8:
  Hands-on lab
  Wrap up with evaluation forms
  
 
 
 

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