用扩散模型评估结构监测数据质量，抗异常值能力强。
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
- 基于条件扩散模型，融合时序上下文与分位数归一化。
- 每个数据点生成异常概率，整体数据集得分更准确。
- 适合真实结构监测场景，对异常值鲁棒性强。
数据质量评估是确保后续结构健康监测（SHM）任务可靠性的关键步骤。本文提出一种基于预测偏差的SHM数据质量评估方法，采用单变量隐式自回归模型，实现异常值诊断与数据清洗。所提出的条件扩散模型（CDM）在标准扩散模型基础上引入条件嵌入模块以融入时序上下文，采用四分位数归一化缓解分布偏斜，并使用Huber损失增强对异常值的鲁棒性。在此单变量隐式自回归框架中，每个数据点被赋予异常概率，量化其“异常程度”，并计算全局质量评估分数以表征整个数据集的质量。利用实际结构的运行数据进行的大量案例研究显示，该框架显著提升了数据质量评估的准确性，优于代表聚类、孤立性检测及深度重建方法的多种强基线。消融实验与超参数分析进一步验证了该框架的有效性与鲁棒性。
原文摘要 · Abstract (English)
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. The proposed conditional diffusion model (CDM) augments the standard diffusion model with a conditional embedding module to incorporate temporal context, quartile normalization to mitigate distribution skew, and a Huber loss to enhance robustness against outliers. Within this univariate implicit autoregressive framework, each data point is assigned an outlier probability, quantifying its degree of "outlier-ness", and a global quality evaluation score is computed to characterize the overall dataset quality. Extensive case studies utilizing operational data from real-world structures demonstrate that the proposed framework significantly improves the accuracy of data quality assessment, outperforming other strong baselines representative of clustering, isolation-based, and deep reconstruction methods. The effectiveness and robustness of the proposed framework are further demonstrated by the findings of ablation experiments and hyperparameter analysis.
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