Fault diagnosis of rotating machinery under imbalanced samples based on outlier-removal HCAB-SMOTE-LOF

In rotating machinery fault diagnosis, the original feature space often suffers from anomalous outliers, limited sample availability, and imbalanced class distributions. These issues significantly impede the accurate recognition of minority-class samples and lead to suboptimal diagnostic efficiency. To tackle these challenges, this paper proposes a hybrid method of HCAB-SMOTE-LOF for diagnosing imbalanced fault diagnosis under outlier contamination. The core idea is not a simple combination of Local Outlier Factor (LOF) and Hybrid Clustering Affinity Synthesis Minority oversampling technique (HCAB-SMOTE), but a coupling mechanism in which LOF-based majority-class purification constrains the boundary region of HCAB-SMOTE, so that synthetic samples are generated in reliable minority-class clusters. To extract the fault-relevant time-frequency features, high-pass filtering is applied to the raw vibration signals to remove low-frequency components. The LOF is introduced to eliminate samples that are clearly abnormal or mislabeled in the feature space. On this basis, the HCAB-SMOTE algorithm is employed to synthetically generate minority-class fault samples, thereby improving the quality and distributional plausibility of the generated samples and alleviating the adverse impact of data imbalance on classifier performance. The effectiveness of the proposed method is validated using both a laboratory dataset and the Ottawa motor dataset. Furthermore, a comparative analysis is conducted between the proposed method and several oversampling strategies applied during data processing, including the use of original data, SMOTE, Borderline-SMOTE, and HCAB-SMOTE. Experimental results demonstrate that the proposed method consistently outperforms all existing oversampling methods across all evaluation metrics, confirming its marked advantages in outlier removal and sample-distribution optimization and thus its capability to effectively detect rotating machinery faults.