Belief function networks for fault diagnosis

Supervisor: Daira Pinto Prieto (d.pintoprieto@vu.nl)

Background

We talk about fault diagnosis in the context of cyber-physical systems. When a cyber-physical systems goes down, it is a big challenge to identify the root-cause of what is failing. Typically, companies offer diagnosis applications that assist the service engineers with identifying such root-cause.

In this context, some sort of reasoning must be encoded into the diagnosis application, aiming to aggregating the available information about the system and its symptoms and providing with a solution. This problem has been tackled in the literature by using Bayesian networks. This approach impose the strong assumptions of probability theory that can be relaxed by using other uncertainty representations, such as belief functions. In this project, we want to explore the advantages of applying belief function networks to this problem, and compare them to Bayesian networks.

Description

This project aims to explore the application of belief function networks to the domain of fault diagnosis. To this end, the student will work with other students on the creation of a small dataset for fault diagnosis and a Bayesian networks that will be used as a baseline model. Afterwards, the student will propose an end-to-end solution for fault diagnosis by applying belief function networks. Some milestones of this project are:

• Creating (collaboratively) a (small) fault diagnosis dataset.
• Creating (collaboratively) a Bayesian network for fault diagnosis.
• Getting familiar with belief function theory.
• Applying Belief Function Machine (implemented in MATLAB) to solve the fault diagnosis problem.
• Analyzing the obtained results.

Notice that this project has a collaborative part with other BSc students.

Literature

Prakash P. Shenoy. Making inferences in incomplete Bayesian networks: A Dempster-Shafer belief function approach. International Journal of Approximate Reasoning, Volume 160, 2023, 108967, ISSN 0888-613X, [PDF].