Bearing Fault Diagnosis Under Data Imbalance and Heavy Noise: An Adaptive Weighted Heterogeneous Ensemble Learning Framework

Recent advances have been achieved in intelligent fault diagnosis of rolling bearings. However, noise interference and data imbalance remain critical challenges for achieving accurate and reliable fault diagnosis in practical industrial applications. To address the limited robustness of conventional deep learning models under noisy conditions and their bias toward majority classes in imbalanced scenarios, this study proposes a robust bearing fault diagnosis method based on an adaptive weighted heterogeneous ensemble learning framework. The proposed method begins with continuous wavelet transform (CWT), which is employed to preprocess raw vibration signals and convert them into time–frequency images. Subsequently, a residual convolutional denoising autoencoder augmented by the convolutional block attention module is developed, namely CBAM-RCDAE. CBAM-RCDAE is capable of effectively reducing and eliminating noise interference in two-dimensional image data, thus enhancing fault diagnosis accuracy. Furthermore, a heterogeneous ensemble learning framework consisting of three base learners, including Swin Transformer, a multi-scale convolutional neural network, and BiLSTM, is developed to enhance generalization capability. An adaptive weight selection (AWS) strategy is introduced to adjust the weights and aggregate the outputs of the three base learners for final fault classification. The proposed method is extensively evaluated on the PU and CWRU bearing datasets. Experimental results demonstrate that, under the most challenging imbalanced conditions, the proposed method improves the G-mean metric by 5.89% and 4.95% compared with the state-of-the-art methods on the PU and CWRU datasets, respectively. In addition, the proposed method exhibits superior noise robustness, enabling reliable fault diagnosis performance across various noise levels.