A Physics-informed, Transfer Learning Approach to Structural Health Monitoring

One of the main challenges for structural health monitoring (SHM) is a lack of failure data to make accurate health predictions. Obtaining desirable failure data is generally very expensive, given the required testing needed to measure all types of system failures, which may be unfeasible in many health monitoring applications. Machine learning has helped to improve health monitoring performance but is still limited by the availability, relevance, and quality of the training data. This data dependence impedes data-driven models from generalizing to unseen data, which is problematic for datasets lacking failure data. Physics-driven models, like finite-element models, are powerful tools for predicting structural responses when the governing physics are not clearly defined. These models can generate simulated fault data to address the data limitation without having to physically damage a structure, but are computationally expensive and susceptible to modeling errors that can prevent the data from being statistically comparable to experimental data. A new trend has been to develop physics-guided machine learning models (PGML), a hybridization of the two aforementioned models that have been shown to improve generalization of, and even outperform, pure data-driven models while using less training data. These PGML models can take many forms, but generally embed some form of physics into a data-driven model as physically relevant constraints. Our research plan is to utilize PGML to improve neural network capabilities to predict structural damage. The proposed PGML model will follow a neural network architecture found in related literature consisting of feature extraction, physics-informed, and label prediction layers. The physics-informed layer will consist of an aggregate of sub-networks trained from simplified structure models which have known governing equations and can be used to generate simulated training data. The full PGML model will use transfer learning to bridge the connections between the untrained layers to the physics-informed layer using experimental data from more complex structures. We will verify our model using publically available SHM datasets used in a variety of past literature experiments.