Measuring data similarity in population-based structural health monitoring using distance metrics

Abstract

Population-based structural health monitoring (PBSHM) expands structural health monitoring (SHM) from a single structure to a group of structures, allowing inferences to be made within and between populations by transferring knowledge across them. Within the populations of interest, the similarity of structures, via their corresponding data, should be assessed to successfully implement PBSHM. This paper focusses on using distance metrics to assess similarity at the very start of the analysis chain, to discover information about a population for which there is little prior knowledge and before any analysis has taken place on individual structures. By doing so, it is possible to quickly and automatically identify abnormalities within the population, group similarly behaving structures together, and inform further decisions. The suitability of several candidate metrics that are not widely employed in SHM are tested using a number of commonly occurring feature behaviours, such as varying amplitudes and temporary mean shifts. The effect of data normalisation/standardisation on the metrics is also explored to identify interesting behaviours within the data. A case study is then presented where distance metrics are used to discover similarities and dissimilarities within temperature data from turbines in an offshore wind farm.