| Citation: | WANG Jiaxin, WEI Jian’an, HUANG Haisong, et al. IAOTE: an adaptive fault diagnosis method for rotors under hybrid imbalanced small samples[J]. Journal of Aerospace Power, 2025, 40（4）：20240507 doi: 10.13224/j.cnki.jasp.20240507
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Improved adaptive oversampling technique (IAOTE) was proposed to address the problems of imbalanced health state data and ineffective mainstream sampling algorithms in the early steam turbine rotor self-scaling fault diagnosis modeling. This technique integrated the adaptive noise-immunity (NI) processing strategy with the improving adaptive semi-unsupervised weighted oversampling (IA-SUWO). Firstly, the NI strategy was used to denoise the mixed imbalanced data, and then the K-means clustering algorithm was used to process the denoised data to recognize the imbalanced data. The NI strategy was used to denoise the blended imbalanced data, and then the K-means clustering algorithm was used to process the denoised data to identify the cluster boundary samples and embed the IA-SUWO mechanism to synthesize the new samples. Furthermore, IAOTE was integrated with least squares support vector machines (LS-SVM) classifiers to construct a fault diagnosis framework to ensure classification interpretability and integrate parameter optimization mechanisms. Experiments showed that among 20 diagnostic examples, IAOTE achieved the highest sensitivity index of 99.76%, the highest G-mean index of 99.25%, the highest F-measure index of 99.42%, the highest area under curve (AUC) index of 99.26%, and the lowest cumulative error, which made IAOTE more suitable for early rotor fault diagnosis in small samples under the mix of imbalance.
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