Tutorials

One of the unique features of the PHM conferences is free technical tutorials on various topics in health management taught by industry experts. As educational events tutorials provide a comprehensive introduction to the state-of-the-art in the tutorial’s topic. Proposed tutorials address the interests of a varied audience: beginners, developers, designers, researchers, practitioners, and decision makers who wish to learn a given aspect of prognostic health management. Tutorials will focus both on theoretical aspects as well as industrial applications of prognostics. These tutorials reach a good balance between the topic coverage and its relevance to the community.

Tentative tutorial topics:

Introduction to Prognostics

Abstract: This tutorial will focus on the fundamentals and basic concepts of prognostics and health management, giving emphasis to condition-based approaches. The audience will be introduced to the key elements that compose a prognostic framework, their interaction, uncertainty and effect on the prediction of the system evolution over time. The session will continue with an overview of data-driven and model-based approaches for prognostics, and will also propose two case studies on prognostic and failure prediction written in Python programming language. The participants will have direct access to the Python scripts and will be able to run them on their personal laptop*. The tutorial will summarize the theory behind the two algorithms, and will guide the audience through the code for a thorough understanding, from data preprocessing to output representation.

*The examples will require Python 2.6 or later, and libraries NumPy, SciPy, and matplotlib, installed on the machine.

Deep Learning for Prognostics

Abstract: Developments in Neural Networks have brought over the past decade a variety of solutions to problems in many fields. Neural Networks are now able to classify, recognize or segment thousands of objects in images, play games, generate new engineered products, translate languages, recognize and translate voices or gestures… Yet, in PHM, the use of neural networks is still quite limited. In this tutorial, we will first explore the fundamentals of deep learning and of its most successful applications. Understanding the strength and the limitations of Neural Networks will help us to understand why their application to PHM is not straightforward. Toy examples will be used to help familiarize with these concepts.

  
 
 
 

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