MBDlyb

MBDlyb features a function-based modelling paradigm in which the system functional decomposition and the inter-functional dependencies of high-tech systems are formalized and use for diagnostic purposes. The knowledge can be used during:

• Design of the system, to gain insight in failure observability and make early design changes to reduce the diagnostic efforts during operation.

• System operation, to assist a service engineer in identifying the root cause of a system failure.

The main features of MBDlyb are

• The use of a single knowledge base for design for diagnostics and operational diagnostics.

• Step-by-step assist a service engineer by showing the most likely faulty components and the most promising diagnostic tests by balancing expected information gain from conducting the diagnostic test with the cost of conducting it.

• Generate a-priori diagnostic procedures in the form of decision trees for fault diagnosis.

• Structured transformation of function-based knowledge to probabilistic graphical models, such as Bayesian networks or Markov networks.

• Persistent storage of the knowledge base in a graph database. 

• Functionality to import a Capella system architecture model.

• A web interface that integrates the model editor, analysis tool and diagnostic assistant.

MBDlyb has been developed by TNO-ESI in the SD2Act project together with ASML. The library is an implementation of the diagnostic methodology proposed in the project. To allow for broader collaboration with other companies as well as academia, the library has been open-sourced under EPL-2.0 license.

• SD2Act 2024: Guided diagnosis of functional failures in cyber-physical systems
  TNO report, 2025 pdf

• Leveraging System Architecture Models for Diagnosis of High Tech Systems:
  Capella Days 2024 pdf, Capella Days 2024 recorded presentation