A sampling-based approach for information-theoretic inspection management

Abstract

A partially supervised approach to Structural Health Monitoring is proposed, to manage the cost associated with expert inspections and maximize the value of monitoring regimes. Unlike conventional data-driven procedures, the monitoring classifier is learnt online while making predictions—negating the requirement for complete data before a system is in operation (which are rarely available). Most critically, periodic inspections are replaced (or enhanced) by an automatic inspection regime, which only queries measurements that appear informative to the evolving model of the damage-sensitive features. The result is a partially supervised Dirichlet process clustering that manages expert inspections online given incremental data. The method is verified on a simulated example and demonstrated on in situ bridge monitoring data.

A probabilistic approach for acoustic emission based monitoring techniques: With application to structural health monitoring

“…This approach is based on that followed in [41], where a constrained DP-based model is constructed in a semi-supervised monitoring scheme, and the information efficiency is used to determine when to inspect the condition of a given measurement. Here, the configuration is unsupervised, and class labels are not required for the assignment of a sample-point to a cluster; this is a convenient property, since one is not required to know a-priori all the sources of AE that may exist in the system.…”