基于最小偏差LS-SVR的模电系统多故障诊断

为解决支持向量分类机多分类存在的样本重复训练、训练模型过多的问题,保证模拟电子系统在整体和局部多故障模式上的诊断正确率,提出基于最小偏差的最小二乘支持向量回归机多故障诊断方法.通过引进样本各维度拟合误差相对于平均拟合误差的偏差平方项,最小化维度间的拟合误差间距,得到能够输出多维变量及具有高分辨率的最小二乘支持向量回归机模型.将模型多维输出值与预设的各个多故障模式值相乘,所得结果集中最大值所对应的多故障模式即为最终诊断结果.仿真结果表明:提出的方法在简化训练过程的同时,能够保持良好的整体和局部多故障诊断正确率.

Analog electronic system multiple fault diagnosis based on minimizing-deviation LS-SVR

Aiming at multi-duplicated samples training and excessive training models in the process of multi-classification with standard support vector machine, and insuring high integral and partial diagnosis accuracy for analog electronic system, a multiple fault diagnosis method based on minimizing-deviation least squares support vector regression (MDLS-SVR) was proposed. With the square of deviation between the dimension fitting error and the average fitting error of the sample introduced to mimize the spacing between each dimension of the output variable, a new multiple-output least squares support vector regression was finally obtained which had high resolution for prediction results. Then through multiplying the model output by pre-set multi fault modes, the corresponding multiple fault mode to which the maximum value in the result set mapped was just the final diagnosis result. The simulation results show that, the new method simplifies the training process and could keep high integral and partial diagnosis accuracy under small sample set.