Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model

2026-04-30 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Structural Health Monitoring · Depth: Expert, quick

Summary

A new method for probabilistic data quality assessment in structural health monitoring (SHM) data, called the conditional diffusion model (CDM), has been developed. This univariate implicit auto-regressive model is designed for outlier diagnosis and data cleaning. The CDM enhances standard diffusion models by incorporating a conditional embedding module for temporal context, quartile normalization to address distribution skew, and a Huber loss function for improved robustness against outliers. Each data point receives an "outlier-ness" probability, and an overall dataset quality score is calculated. Case studies using real-world operational data demonstrate that this framework significantly improves data quality assessment accuracy, surpassing established clustering, isolation-based, and deep reconstruction methods. Ablation experiments and hyperparameter analysis further confirm its effectiveness and robustness.

Key takeaway

For structural health monitoring engineers and data scientists tasked with ensuring data reliability, adopting the proposed conditional diffusion model can significantly enhance the accuracy of outlier detection and data cleaning. Your data quality assessment workflows will benefit from its robust handling of temporal context and skewed distributions, leading to more dependable SHM analyses and predictions. Consider integrating this probabilistic approach to quantify data point "outlier-ness" and improve overall dataset quality scores.

Key insights

A conditional diffusion model improves SHM data quality assessment by robustly identifying and quantifying outliers.

Principles

• Temporal context improves outlier detection.
• Robust loss functions mitigate outlier impact.
• Probabilistic scoring quantifies "outlier-ness".

Method

The CDM integrates conditional embedding for temporal context, quartile normalization for skew, and Huber loss for outlier robustness within a univariate implicit auto-regressive framework to assign outlier probabilities and global quality scores.

In practice

• Apply CDM for SHM data cleaning.
• Use quartile normalization for skewed data.
• Implement Huber loss for outlier-prone datasets.

Topics

• Structural Health Monitoring
• Data Quality Assessment
• Conditional Diffusion Model
• Outlier Diagnosis
• Univariate Auto-regressive Model

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.