A locally unsupervised hybrid learning method for removing environmental effects under different measurement periods

•Proposing a locally unsupervised hybrid learning method suitable for different measurement periods and data dimensions.•Leveraging some advanced machine learning algorithms including hybrid learning, local learning, and dictionary learning.•Proposing a novel dictionary learning algorithm with both discriminative and reconstructive abilities under the theory of sparse subspace learning.•Removing various environmental effects from modal frequencies with high accuracy. Environmental effects induce deceptive variability in unlabeled vibration data for structural health monitoring (SHM). Although unsupervised learning is an effective solution to this issue, some new challenges such as the size of training data in different measurement periods and the type of learning between local and global frameworks seriously affect overall performance of this technique. To tackle these issues, we propose a locally unsupervised hybrid learning method based on an innovative discriminative reconstruction-based dictionary learning (DRDL) algorithm. The proposed method initially uses a Gaussian mixture model to provide local information for the DRDL algorithm by clustering entire training data into local subsets. Subsequently, this algorithm computes sub-dictionaries and sparse coefficients to reconstruct local training subsets. Using these subsets, an anomaly detector developed from the Mahalanobis-squared distance is used to determine damage indices for SHM. Real data from two bridges are incorporated to verify the proposed method with some comparisons.