The 10th International Conference on Energy, Resources, Environment and Sustainable Development (ERESD 2024)

259 / 2024-03-14 16:08:16

Research on Fault Diagnosis of Coal Mine Fan Bearing Based on ELM and SSA

Bearing fault diagnosis; extreme learning machine; sparrow search algorithm

Intelligent Equipment Technology > 7．Equipped with Safety Monitoring and Control Technology

Bearing is the core component of coal mine ventilator，failure may affect the operation of coal mine ventilator. decomposing its vibration signal, extracting characteristic information and modeling fault diagnosis realize the fault diagnosis of coal mine ventilator bearing. For the vibration signals obtained from signal decomposition, accurate vibration signals are extracted by optimizing the calculation parameters. It is necessary to complete the classification of various feature signals in different states with high accuracy. ELM is selected as the base classification algorithm based on the advantages of less training parameters, fast learning speed and strong generalization ability of extreme learning machine (ELM). To address the problem that randomly generated input weights and implied layer thresholds affect the classification accuracy of ELM, Tent chaotic mapping, uniformly distributed dynamic adaptive weight factors and the Corsi-Gaussian variational multi-strategy enhanced sparrow search algorithm (MSSSA) are introduced to find the optimization of ELM parameters; then, the MSSSA-ELM fault diagnosis model is established and compared with SVM, ELM, Then, the MSSSA-ELM fault diagnosis model was developed and compared with SVM, ELM, PSO-ELM and SSA-ELM models for fault classification experiments. finally, a coal mine ventilation fan bearing fault diagnosis system was designed using MATLAB GUI tool, it is verified that the MSSSA-ELM fault diagnosis model has higher classification accuracy.