基于多尺度时域平均分解和模糊熵的船用风机故障诊断方法

船舶旋转机械往往在多振源的环境中运转，如何对其振动信号进行有效的特征提取和降噪处理是研究热点。时域同步平均方法对振动信号的噪声抑制有较好的效果，但是该方法需要的键相信号难以获取，特定频率以外的故障信息会丢失且倍频信号波形会互相混叠，以上问题限制了该方法的适用性。提出了一种多尺度时域平均分解法，有效地克服了传统时域平均法存在的问题，并结合模糊熵特征选择对船舶风机进行了故障诊断，准确率与计算速度均优于EMD、EEMD和VMD方法。仿真数据分析与故障模拟试验证明了该方法的有效性。

Fault Diagnosis Method of Marine Fans Based on MTAD and Fuzzy Entropy

Ship rotating machinery often operated in a multi-vibration source environment. How to effectively extract features and reduce noise processing of the vibration signals was a hot topic for scholars. The time-domain synchronous averaging method had good effect iveness on the noise suppression of vibration signals. However， the key phase signals required by this method were difficult to obtain， and fault information other than the specific frequency would be lost， and the waveforms of the frequency multiplication signal would be mixed with each other. The above problems limited the applicability of this method. A MTAD method was proposed to overcome the problems of the traditional time-domain averaging method effectively， and combined fuzzy entropy feature selection to perform fault diagnosis on ship wind turbines. The accuracy and calculation speed are better than that of EMD， EEMD and VMD method. The effectiveness of the proposed method is demonstrated through simulation data analysis and fault simulation experiments.