Active Learning Approaches to Structural Health Monitoring

Abstract

A critical issue for structural health monitoring (SHM) strategies based on pattern recognition models is a lack of diagnostic labels for system data. In an engineering context these labels are costly to obtain, and as a result, conventional supervised learning is not feasible. Active learning tools look to solve this issue by selecting a limited number of the most informative data to query for labels. This article demonstrates the relevance of active learning, using the algorithm proposed by Dasgupta and Hsu (the DH active learner). Results are provided for applications of this technique to engineering data from aircraft experiments.