基于随机共振和多维度排列熵的水电机组振动故障诊断

针对强噪声背景下难以提取水电机组振动故障特征的问题,提出了一种基于随机共振(SR)去噪和多维度排列熵(MPE)提取振动信号特征向量的故障诊断方法。首先,采用随机共振对振动信号进行去噪,增强信号的信噪比;继而利用多维度排列熵提取去噪信号的特征向量,最后将其输入所建立的改进粒子群算法优化支持向量机(PSO-SVM)模型,实现故障的识别与诊断。仿真结果表明,该方法具有较高的诊断精度。

Vibration fault diagnosis of hydropower unit by using stochastic resonance and multidimensional permutation entropy

Aiming at the issue that the characteristics of hydropower unit vibration faults are difficult to extract under strong background noises, this paper presents a fault diagnosis method using the techniques of stochastic resonance (SR) denoising and multidimensional permutation entropy (MPE) for extraction of the characteristic vectors from vibration signals. This method first denoises a vibration signal using stochastic resonance to enhance its stochastic resonance, then uses MPE to extract its feature vectors. Taking the feature vectors as input, an improved particle swarm algorithm and support vector machine model is able to achieve identification and diagnosis of the signal faults. Our simulations show that the method enables the fault diagnosis of hydropower units with high accuracy.