Possibilistic 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 possibility functions. In this project, we want to explore the advantages of applying possibilistic networks to this problem, and compare them to Bayesian networks.

Description

This project aims to explore the application of possibility 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 possibilistic networks. Some milestones of this project are:

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

Literature

BenAmor, N., Dubois, D., Gouider, H., Prade, H. (2014). Possibilistic Networks: A New Setting for Modeling Preferences. In: Straccia, U., Calì, A. (eds) Scalable Uncertainty Management. SUM 2014. Lecture Notes in Computer Science(), vol 8720. Springer, Cham. [PDF]