High-Speed Elevator Fault Generation and Diagnosis Method: A Scalable Model-Driven Approach With Variational-Mode-Decomposition Graph Convolution Network

Elevator faults can cause significant harm, especially in high-speed elevators. However, obtaining fault samples from in-service high-speed elevators by monitoring sensors is challenging due to high simulation costs and limited efficiency, which hinders the development of fault diagnosis. To address this, a scalable fault simulation model is proposed, offering a cost-effective and efficient approach to generate fault samples. Leveraging this model, an innovative fault methodology that combines variational mode decomposition (VMD) and graph convolutional network (VGCN) is proposed to enhance the extraction and representation of deep features from the fault data. This approach consists of two core components: a variational-mode-decomposition layer (V-Layer) that decomposes and reconstructs vibration signals, effectively mitigating noise interference, and a graph convolution layer (GC-Layer) that uses Hermite polynomials to adaptively fit the graph convolution kernel, ensuring robustness in high-noise conditions. Faults are then classified through a fully connected layer with a softmax classifier. The proposed method is validated using both a simulated fault dataset and a real-world high-speed elevator dataset, demonstrating superior performance in fault diagnosis.