This paper addresses the problem of anomaly detection in multidimensional time series data, where labeled samples are often scarce and data patterns are complex. We propose a semi-supervised learning algorithm that combines labeled and unlabeled data to enhance detection performance. To mitigate the risk of classifier degradation caused by mislabeled data, we introduce a fuzzy clustering-based selection mechanism. The algorithm selects high-confidence samples from the unlabeled data using fuzzy C-means clustering and iteratively adds them to the training set through a self-training process. Experiments on transformer oil chromatography data show that the proposed method achieves better performance than traditional semi-supervised and supervised approaches. The results demonstrate the effectiveness of integrating clustering techniques into semi-supervised learning for robust anomaly detection under limited label conditions.