A Novel Fault Diagnosis Method for Road Tunnel Ventilation Equipment Based on EEMD-SE Model

The operation status of ventilation equipment has a crucial impact on the safety of road tunnel traffic. In order to monitor the fault status of ventilation equipment, this paper determines the fault types and vibration signal data that need to be simulated according to the structural characteristics of axial fan, and puts forward a fault diagnostic model based on Ensemble Empirical Mode Decomposition-Squeeze and Excitation (EEMD-SE), which is based on the mechanism of ensemble empirical mode decomposition and attention. Fault diagnosis model, through the design and construction of the fan fault test bench to complete the fault simulation, while the collection and storage of vibration signals and various states of the fan data collection of empirical mode decomposition and analysis, the decomposed signal using the attention mechanism to further feature extraction and fault type discrimination, after verification of the model accuracy of up to 99.977%, has a good fault diagnosis, compared with other classical algorithms have a higher accuracy and accuracy, and the model has a higher accuracy and accuracy. The model is validated to be 99.977% accurate, with good fault diagnosis effect, compared with other classical algorithms with higher accuracy rate.