Embedding Diagnosability of Complex Industrial Systems Into the Design Process Using a Model-Based Methodology

Authors: Leonardo Barbini, Carmen Bratosin and Thomas Nägele

Proceedings 6th European Conference of the Prognostics and Health Management Society (PHME 2021), pages 1-9, 2021.

Abstract: There is a constant increase of the market expectations on the capabilities of industrial high-tech systems. To meet these expectations, designers of such systems have to continuously explore complex solutions that ensure both functionality and maximum up-time. In this paper we describe a methodology that supports the designers in this difficult task. Specifically, we introduce a model-based approach that computes both the diagnosability of a system and the list of service actions to perform in order to find the root cause of any of the system’s failures. The proposed methodology starts at design time, by creating behavioral models for the spare parts of the system. These models specify both the expected behavior and possible Failure Modes (FMs) of the spare parts. Using these models, the system design is composed, with the individual spare part behaviors defining the system’s behavior. To create these models we use a domain-specific language that generates a Bayesian belief Network (BN) that allows for the simulation of the behavior of the full system. The BN is used to compute the failure symptoms, i.e. readings on a given sensor configuration, for every FM in the system. Finally, we perform the diagnosability analysis by determining FMs for which the symptoms are equal, causing them to be unidentifiable. For the unidentifiable FMs, we compute a set of service actions needed to ensure full diagnosability and the corresponding sensor readings to differentiate between the failures. This information is then used by the designer to compare different designs of the system. In this paper, we illustrate our approach on a sub-system of a high-tech machine, but the methodology is domain independent.