Hierarchical model verification and validation for structural health monitoring using dynamic substructuring

Despite the success of data-based methods in structural health monitoring (SHM), these approaches often suffer from a lack of training data, which can be difficult to acquire for several reasons: damage-state data acquisition can be infeasible, structures may be unique and only tested in situ, sensor placement can cause issues, certain structures cannot be tested in controllable laboratory conditions and representative environmental conditions can be difficult to simulate. Training data can be simulated using physics-based models. However, this is dependent on model verification and validation (V&V), meaning assembly-level data is still required.

Hierarchical V&V is a novel technique in the field of SHM. The aim of hierarchical V&V is to remove the necessity for assembly-level validation data. Instead, the process entails the V&V of subassembly-level models, which are then combined to produce an assembly-level model using dynamic substructuring (DS). This simplifies the data acquisition process in order to reduce the associated difficulties and costs.

This paper focuses on the role of DS in the hierarchical V&V process for SHM. DS allows substructures to be used to create an assembly model, and for simultaneous uncertainty propagation. This allows confidence to be established in the assembled models without requiring assembly-level data.