AI RESEARCH
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
arXiv CS.LG
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ArXi:2604.26366v1 Announce Type: cross Data quality assessment is an essential step that ensures the reliability of the subsequent structural health monitoring (SHM) tasks. This study proposes a prediction deviation-based SHM data quality assessment method using a univariate implicit auto-regressive model, enabling outlier diagnosis and data cleaning.