A transfer learning method based on MC-1DCNN for elevator guideway fault diagnosis

Elevator guideway vibration fault diagnosis is essential for ensuring elevator safety and stability. However, vibration signals exhibit complex non-stationary characteristics, and abnormal vibration samples are limited. This paper proposes an advanced fault diagnosis method combining Multi-Channel One-Dimensional Convolutional Neural Networks (MC-1DCNN) with transfer learning for elevator guideways. 1D-CNN is employed to extract local temporal correlations in vibration signals, while Empirical Mode Decomposition (EMD) decomposes signals into Intrinsic Mode Functions (IMFs) to provide multi-frequency features as multi-channel inputs. The MC-1DCNN is pre-trained on the Case Western Reserve University (CWRU) bearing fault dataset to learn general mechanical fault features and is then fine-tuned on the elevator guideway dataset by freezing lower convolutional layers and adjusting higher layers. Experimental results demonstrate that the proposed method achieves high classification accuracy and fast convergence in small-sample scenarios. This study validates the effectiveness of transfer learning in elevator fault diagnosis and provides a practical solution for real-world applications.