PGDS-CLNet: A robust bearing fault diagnosis method under varying loads and strong noise

Abstract

In practical industrial applications, bearing fault diagnosis techniques frequently suffer from poor accuracy and limited generalization due to variable operating conditions, severe background noise, and weak fault vibration signatures. To address these critical challenges, a novel intelligent fault diagnosis framework, termed PGDS-CLNet, is proposed in this study. Different from traditional methods relying on handcrafted diagnostic features, the proposed PGDS-CLNet is an end-to-end trainable diagnostic network after standard signal normalization and segmentation. It directly learns discriminative representations from one-dimensional vibration segments without manually designing time-domain, frequency-domain, or time-frequency features. Elegantly extracting robust feature representations through a Prior-Guided Feature Extraction Module (PGFEM) and a Dual-Skip Separable Convolution Unit (DSCU), ultimately achieving precise multi-class fault identification via a Softmax layer. To effectively mitigate overfitting and minimize generalization errors, a comprehensive optimization strategy incorporating a learning-rate adaptive Adam optimizer, dropout, mini-batch training, L2 regularization, and Batch Normalization (BN) is seamlessly integrated into the model training process. Extensive experimental results demonstrate that the proposed PGDS-CLNet exhibits exceptional generalization capabilities under varying load conditions and intense noise environments. Furthermore, experimental results show that PGDS-CLNet achieves testing accuracies of 100.00%, 98.34%, and 98.66% under 0, 300, and 600 N load conditions, respectively, with a mean testing accuracy of 99.00%. Under strong Gaussian noise of −8 dB, the proposed model still achieves 87.24% accuracy, outperforming LSTM-CNN, and WDCNN.