基于遗传退火优化MSVM的齿轮箱故障诊断

为了实现齿轮箱典型故障的自适应准确辨识，提出一种遗传退火算法优化多核支持向量机的齿轮箱故障诊断模型。首先，将齿轮箱故障振动信号经验模式分解为多个内禀模态分量并提取其幅值能量特征；然后，再基于高斯核和多项式核构建多核支持向量机；最后，将表征齿轮箱故障特征的内禀模态分量能量输入到遗传退火算法优化的多核支持向量机进行故障模式辨识。理论分析表明，多核支持向量机能够逼近任意多元连续函数，遗传退火参数优化可快速准确得到多核支持向量机的全局最优参数向量。通过齿轮箱的故障模拟实验验证了该方法的有效性，结果表明，相比于传统的故障诊断模型，该方法显著提高了齿轮箱典型故障的诊断精度和泛化推广能力。

Gearbox Fault Diagnosis Based on Multi-kernel Support Vector Machine Optimized by Genetic Simulated Annealing Algorithm

The multi-kernel support vector machine optimized by a genetic simulated annealing algorithm is proposed to effectively identify complex fault characters for the gearbox fault diagnosis. Fault vibration signals are processed by empirical mode decomposition to obtain several stationary intrinsic mode functions. Then, the instantaneous amplitude energy of the intrinsic mode functions are computed and regarded as the input characteristic vector of the multi-kernel support vector machine optimized by the genetic simulated annealing algorithm for fault classification. The multi-kernel support vector machine can fit an arbitrary function within a high-dimensional feature space. The genetic simulated annealing algorithm demonstrates a superior performance of global optimization and convergence speed, and can improve the diagnosis performance and robustness of the gearbox fault diagnosis model. The gearbox fault diagnosis experiment thus demonstrates the effectiveness of this novel method.