Rolling bearing fault diagnosis based on VMD-CNN-BiLSTM

Rolling bearings are critical components in rotating machinery, directly affecting the efficiency and reliability of the equipment. However, they are prone to various faults under complex conditions and strong noise backgrounds. Traditional fault diagnosis methods often struggle to accurately extract fault features from these complex signals, resulting in low diagnostic accuracy. This paper proposes a comprehensive algorithm combining Variational ModeDecomposition (VMD), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM)network, referred to as VMD-CNN-BiLSTM, for rolling bearing fault diagnosis. VMD decomposes complex signals into multiple Intrinsic Mode Functions (IMFs), reducing signal modal aliasing and endpoint effects. The CNN layer extracts local features, and the BiLSTM layer captures temporal dependencies and bidirectional relationships within the signals, achieving accurate fault feature recognition. The innovation lies in integrating VMD with CNN and BiLSTM, optimizing signal decomposition and feature extraction processes to enhance fault diagnosis accuracy and robustness. The algorithm's effectiveness is validated on the Case Western Reserve University bearing dataset with a rotational speedof1797 r/min, including ten fault conditions. The Arctic Puffin Optimization algorithm optimized VMD parameters, and optimal IMF components were identified using minimum envelope entropy. Feature extraction resulted in 1200 samples, divided into training and test sets. Experimental results demonstrate that the VMD-CNN-BiLSTM model achieves a diagnostic accuracy of 98.6667% with a training time of 9.5667 seconds. This method accurately extracts fault features in complex and noisy environments, significantly improving fault diagnosis performance and reliability.