Abstract: This study presents TL-MC-ShuffleNetV2, a lightweight and transferable fault diagnosis framework designed for elevator guideway vibration analysis. To tackle challenges such as limited labeled data and the constraints of real-time deployment, the approach integrates Variational Mode Decomposition (VMD) for multi-scale signal separation and employs a customized 1D ShuffleNetV2 backbone with multi-channel (MC) inputs. Squeeze-and-Excitation (SE) attention modules are embedded throughout the network to enhance channel-wise feature sensitivity. A transfer learning (TL) strategy is adopted, in which the model is initially trained using the Case Western Reserve University (CWRU) bearing dataset and subsequently adapted to the elevator domain by freezing early convolutional layers while fine-tuning higher-level layers. Evaluation results demonstrate that the proposed framework achieves a classification accuracy of 96.4%, alongside significantly reduced inference time and parameter complexity. Comparative and ablation experiments further validate the individual contributions of VMD preprocessing, SE modules, and transfer learning to model performance. Overall, the method exhibits strong adaptability, computational efficiency, and suitability for deployment in smart elevator monitoring systems under Industry 4.0 environments.

Zhiwei Zhou, Xianghong Deng, Xuwen Zheng and Chonlatee Photong, “TL-MC-ShuffleNetV2: A Lightweight and Transferable Framework for Elevator Guideway Fault Diagnosis” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160646

@article{Zhou2025,
title = {TL-MC-ShuffleNetV2: A Lightweight and Transferable Framework for Elevator Guideway Fault Diagnosis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160646},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160646},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Zhiwei Zhou and Xianghong Deng and Xuwen Zheng and Chonlatee Photong}
}