A Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and a Multi-Layer Convolutional Temporal Network

Abstract

Fault signals of rolling bearings in wind turbine drivetrain systems often exhibit characteristics such as nonlinearity, non-stationarity, and low signal-to-noise ratio. Under complex operating conditions, traditional signal processing methods and shallow classification models struggle to maintain high recognition stability and diagnostic accuracy. To address this issue, this paper proposes a fault diagnosis method that combines Variational Mode Decomposition with a deep convolutional bidirectional long short-term memory network. After data augmentation is applied to the original vibration signals, VMD is used to decompose the samples into multiple intrinsic mode functions, which are then used to construct multichannel temporal inputs. Furthermore, a VMD-CNN-SE-BiLSTM network is designed, in which multi-layer onedimensional convolutions are employed to extract local fault features, the SE attention mechanism is introduced for channel recalibration, and BiLSTM is incorporated to capture temporal dependencies in deep features. Finally, a global average pooling layer over time and a fully connected layer are used to classify fault categories. Experimental results on the Case Western Reserve University dataset show that the proposed method achieves a recognition accuracy of 99.4%. By integrating modal decomposition, local feature representation, and temporal dependency modeling, the proposed method provides an effective solution for rolling bearing fault diagnosis under complex operating conditions.