数据驱动算法在旋转机械故障诊断中的应用研究

传统故障诊断方法依赖于先验数据与模型，具有局限性。为解决此问题，提出一种基于数据驱动的旋转机械故障诊断方法。利用经验模式分解（EMD）算法拆分原始故障信号，得到有限个IMF分量，优化现有EMD算法得到最优的截断阈值，并有效分离系统噪声干扰；从多域量化角度提取故障信号的时域、频域特征，并基于EMD样本熵实现对去噪旋转机械故障信号中故障点特征的分类与识别。仿真结果表明：所提出的数据驱动算法能够准确地识别出不同载荷条件下的故障信号微弱特征，具有更高的训练精度和故障诊断精度

Application of Data-driven Algorithms in Fault Diagnosis of Rotating Machinery

Traditional fault diagnosis methods rely on prior data and models, which have limitations.To solve this problem, a data driven fault diagnosis method for rotating machinery was proposed. The empirical mode decomposition (EMD) algorithm was used to decompose the original fault signal, and the limited IMF components were obtained. The optimal cut off threshold was obtained by optimizing the existing EMD algorithm, and the system noise interference was effectively separated. The time domain and frequency domain features of the fault signals were extracted from the multi-domain quantization perspective, and the feature classification and identification of the fault points in the fault signal of de noising rotating machinery were realized based on the EMD sample entropy.The simulation results show that the proposed data driven algorithm can be used to accurately identify the weak features of fault signals under different load conditions, and it has higher training accuracy and diagnostic accuracy