Short Course

At PHMAP19 (July 23-25, 2019) on July 21-22 in Beijing, China

At PHM19 (September 21-26, 2019) on September 21-22 in Scottsdale Arizona, USA

Program Download(English)

PHM Fundamentals: Monitoring/Sensing to Fault Diagnosis, Failure Prognosis and Case Studies

This introductory course will be taught by recognized international experts in the PHM field and will cover the current state of the art in PHM technologies, sensors and sensing strategies, data mining tools, CBM+ technologies, novel diagnostic and prognostic algorithms as well as a diverse array of application examples/case studies. It is addressed to engineers, scientists, operations managers, educators, small business principals and system designers interested to learn how these emerging technologies can impact their work environment.  Through a lecture (with Q&A), networking and workshop format with several specialist experts, you will:
  1. Establish a baseline for defining the extent and capabilities of PHM, specifically needs and organization
  2. Identify specific details of PHM Applications (metrics, sensors, cost benefits, reliability) and PHM Methods (diagnostics, prognostics, data driven methods and uncertainty)
  3. Examine case studies of PHM applications across diverse domains to identify solutions and impacts
  4. Plan a PHM application in two mini workshop settings with expert group leaders
  5. Identify issues and needs and a way forward including Continuing Professional Development
■ Topics include
No Topics
1 Introduction to PHM (Taxonomy, scope, basics, standards- for all talks)
2 Deriving Requirements for PHM
3 PHM Performance Metrics
4 Diagnostics Methods
5 Case Study for requirements/metrics (Description of an application)
6 Prognostics
7 Data Analytics Methods
8 Prognostics Case Studies (2 case studies for prognosis and data analytics information)
9 Sensors and Data Processing (Available/Required data and organization)
10 Analysis mini workshop (Small group data design activity with worksheets)
11 CBM+ and IVHM Technologies
12 PHM Cost Benefit Analysis
13 Plenary- Issues and Needs (Review to compile collected issues from all participants)
14 Reliability and Life Cycle Management (Linking reliability and PHM approaches)
15 Case Study Workshop Introduction (Small group activity builds on data design mini)
16 Fielded Systems Case Studies-1(2 case studies for CBM and Reliability)
17 Fielded Systems Case Studies-2 (3rd case study for CBA)
18 Case Study Mini workshop (Small group activity and reporting)
19 Way forward (Paths, Resources, Continuing Professional Development)

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.

■ Topics include
No Topics
1 Overview of data-driven PHM
2 Review of Fundamental statistics
3 Data Visualization
4 Machine learning - introduction and concepts
5 Data transformation & feature extraction
6 Classification
7 Regression
8 Introduction to Neural Networks
9 Hands-on Lab
10 Feature selection
11 Characterizing performance
12 Model Selection
13 Anomaly detection
14 Deep Learning I
15 Deep Learning II
16 Applications
17 Practical matters
18 Hands-on Lab

Lecturer: Dr. Neil Eklund

… Bottom Line: Education is important to The PHM Society

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 local coordinators: Jeff Bird:, Chao Jin: