Data cleaning is essential for ensuring the quality of bridge health monitoring data. Existing methods face challenges in anomaly detection and repair, often constrained by model complexity, overfitting, and inadequate integration of spatiotemporal correlations. To address the challenges, a subsequence-level data cleaning framework integrating anomaly detection and data repair is proposed in the study. Statistical features and wavelet variances are extracted from time-series subsequences. Furthermore, density-based spatial clustering of applications with noise and one-class support vector machine (OC-SVM) are employed to construct a hyperplane decision boundary, enabling efficient and real-time anomaly detection. Subsequently, a pre-trained patch time-series transformer model is used to repair anomalous subsequences by combining the spatial correlations among sensors and the global/local features of time series. Repair quality is evaluated using the OC-SVM decision boundary to select the optimal repair result, achieving an integration between anomaly detection and repair. Field validation through a suspension bridge demonstrates that the proposed method can detect various types of anomalies. The repair error is controlled within 2.61%, confirming its practical applicability in engineering applications.
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