轴流风机故障诊断中的信号特征选择方法研究

Feature Selection Methods for Fault Diagnosis of Axial-Flow Fans

为确保煤矿工作人员的安全及提高生产效率,研究了一种基于多类型信号特征选择的轴流风机故障诊断模型。该模型主要通过监测风机在不同工况下的振动加速度信号,并经过信号处理获得速度信号和位移信号。通过提取这些信号的时频域统计特征,并利用极致梯度提升(Extreme Gradient Boosting,XGBoost)技术进行特征选择,以增强诊断准确性。实验结果显示,通过特征选择优化后的多信号数据集,模型在测试集上的平均判识准确率达到98.33%,对数损失仅为0.0534。相较于单一信号或未进行特征选择的数据集,模型表现出更高的效率和准确度,显著提升了故障诊断的可靠性和速度,从而有效减少了由风机故障可能导致的安全隐患。

A fault diagnosis model for axial flow fans based on multi-signal feature selection has been developed to ensure the safety of coal mine workers and enhance production efficiency.This model primarily monitors the vibration acceleration signals of fans under various operating conditions,processing these signals to obtain velocity and displacement data.The diagnostic accuracy is significantly enhanced by extracting these signals'time-frequency domain statistical features and using Extreme Gradient Boosting(XGBoost)for feature selection.Experimental results indicate that the optimized multi-signal dataset,after feature selection,achieves an average recognition accuracy of 98.33%on the test set,with a log loss of only 0.0534.Compared to datasets using single signals or without feature selection,the model demonstrates higher efficiency and accuracy,significantly improving the reliability and speed of fault diagnosis,thereby effectively reducing the safety hazards potentially caused by fan failures.