Distributed strains sensing (DSS) with distributed fiber optic sensors (DFOS) has great potential for structural health monitoring (SHM). Raw DSS data might contain different types of disturbances caused by the measurement principle of DFOS. The disturbance types are (i) misreadings called strain reading anomolies (SRA), (ii) missing values called dropouts, and (iii) noise. Hence, pre-processing (the process of removing or reducing the disturbances) is key for a reliable evaluation of DSS data. Many different pre-processing approaches/algorithms exist. The assessment, how well an algorithms performs in removing the disturbances is done by benchmarking. This judgement requires a known "ground truth" (disturbance free signal). As all measurements show noise, this benchmarking needs to be carried out on an artifical data set.

The aim of this benchmark data set is to simulate realistic DSS data. The characteristics of the benchmark data set is described in in detail in the accompanying paper available at 10.3390/s24237454. To simulate different use cases, the data set contains five scenarios. SRAs, dropouts and noise are simulated using simple random processes. The values for SRAs are extracted from the data set available at 10.25532/OPARA-671.

This dataset is available at 10.25532/OPARA-644 and accompanies the paper 10.3390/s24237454.