An intelligent fault diagnosis model for bearings with adaptive hyperparameter tuning in multi-condition and limited sample scenarios

Bearing fault diagnosis under multiple operating conditions is challenging due to the complexity of changing environments and the limited availability of training data. To address these issues, this paper presents an advanced diagnosis method using a hybrid Grey Wolf Algorithm (HGWA)-optimized convolutional neural network (CNN) and Bidirectional long short-term memory (BiLSTM) architecture. The proposed model leverages CNN for extracting spatial features and BiLSTM for capturing temporal dependencies. Through HGWA, hyperparameters are efficiently optimized, achieving 100% diagnostic accuracy across four operating conditions with the CWRU dataset. Additionally, the optimized CNN-BiLSTM model demonstrated high diagnostic accuracy when applied as a pre-trained model in new environments, even with minimal training data. The proposed model not only improves diagnostic performance but also enhances optimization efficiency, achieving faster results within the same time frame. This approach mitigates the challenges of manually tuning neural network hyperparameters and effectively addresses bearing fault diagnosis under constrained sample conditions, representing a meaningful contribution to the field of rolling bearing fault diagnostics.