Cross-Scale Time-Frequency Fusion Network for Non-Stationary Vibration Fault Diagnosis of Elevator Door Systems

Abstract

Elevator door systems are critical and failure-prone subsystems in elevators, with vibration signals characterized by strong nonlinearity, non-stationarity, and complex temporal dependencies under varying operating conditions. Existing fault diagnosis methods often struggle to effectively model these characteristics, particularly in capturing cross-scale dependencies between local transient disturbances and global temporal evolution patterns. To address this issue, a cross-scale spatiotemporal feature learning framework integrating adaptive time-frequency decomposition and deep feature fusion is proposed. Variational mode decomposition (VMD) is applied to decompose nonlinear vibration signals into intrinsic mode functions, while fast Fourier transform (FFT) is used to extract global spectral information, forming a multi-channel time-frequency representation. The convolutional neural network (CNN) module is designed to extract local transient features associated with mechanical impacts, while the bidirectional long short-term memory (BiLSTM) module models long-term temporal dependencies. Furthermore, a cross-attention mechanism is introduced to dynamically fuse local and global representations, enhancing discriminative feature learning under complex operating conditions. Experimental results on a representative elevator door fault dataset show that the proposed approach attained an average accuracy of 99.18% and an average F1-score of 99.13%. Compared with conventional machine learning and neural network-based methods, the proposed framework demonstrates superior diagnostic accuracy and robust performance under the evaluated experimental conditions. The findings indicate that the proposed approach serves as an efficient strategy for cross-scale spatiotemporal modeling of nonlinear and non-stationary vibration signals, with considerable promise for intelligent health monitoring and prognostic maintenance of elevator door systems.