应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断

Abstract

针对固有时间尺度分解算法的模态混叠问题和最小二乘支持向量机的参数优化问题，本文提出了一种新的基于完备集合固有时间尺度分解和混合差分进化和粒子群算法优化最小二乘支持向量机的柴油机故障诊断方法。该方法主要包括以下几个步骤：首先，为解决固有时间尺度分解算法的模态混叠问题，提出了一种完备集合固有时间尺度分解算法。随后，利用完备集合固有时间尺度分解算法将非平稳的柴油机振动信号分解为一系列平稳的旋转分量和残差信号。然后，提取了前几阶旋转分量的三类典型的时频特征，包括奇异值、旋转分量能量和能量熵、AR模型参数，作为故障特征。最后，提出了混合差分进化和粒子群算法对最小二乘支持向量机的参数进行优化的方法，并通过将故障特征输入训练好的最小二乘支持向量机模型实现故障诊断。仿真和实验结果表明提出的故障诊断方法可以克服固有时间尺度分解的模态混叠问题，而且能够准确的识别柴油机故障。

Keywords

柴油机 / 故障诊断 / 完备集合固有时间尺度分解 / 最小二乘支持向量机 / 混合差分进化和粒子群优化算法

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