基于多尺度模糊熵和STOA-SVM的风机轴承故障诊断

针对风机轴承振动信号故障特征提取困难的问题，提出了一种基于多尺度模糊熵(MFE)特征提取,并结合乌燕鸥优化算法(STOA)优化支持向量机(SVM)的风机轴承故障诊断方法。首先采集原始振动信号并计算其多层次模糊熵，其次构造故障特征向量集合作为SVM的输入，最后采用STOA优化SVM对轴承故障进行分类诊断。通过凯斯西储大学轴承振动数据进行仿真，结果显示轴承故障诊断准确率达到了99.3〖WTB4〗%〖WTBZ〗，证明了所提方法具有较高的准确度和有效性。

Fan Bearing Fault Diagnosis Based on Multi-Scale Fuzzy Entropy and STOA-SVM

Aiming at the difficulty of fault feature extraction of fan bearing vibration signals, a fault diagnosis method of the fan bearing based on the multi-scale fuzzy entropy (MFE) feature extraction and combined with the sooty tern optimization algorithm (STOA) optimized support vector machine (SVM) is proposed. Firstly, the original vibration signals are collected and the multi-level fuzzy entropy is calculated. Secondly, the fault feature vector set is constructed as the input of SVM. Finally, the STOA is used to optimize SVM for classification and diagnosis of bearing faults. Simulation based on the bearing vibration data from Case Western Reserve University shows that the bearing fault diagnosis accuracy reaches 99.3%, which proves that the proposed method has high accuracy and effectiveness.