基于DBN和EEMD方法的精轧测温传感器故障诊断与分类

为了减少轧制过程温度测量不准引发的钢产品质量问题,该文采用DBN网络和EEMD模态分析相结合的方法,建立精轧后测温传感器故障诊断与分类模型。首先建立DBN的温度预报模型,对精轧后温度进行预测;其次对预报温度与实际温度的差值进行EEMD特征提取;最后,构建DBN故障诊断与分类模型,用方差、方差百分比、能量、能量百分比方法分别或者联合的方法构建特征向量集,并将这些向量集作为故障模型的输入,对正常情况、漂移、精度下降、冲击、固定偏差等故障进行诊断与分类。以六架精轧机组的带钢生产为例进行仿真实验,结果表明,建立的模型具有很好的故障诊断与分类能力。

Optimization of Output Properties for Bias Signal-Modulated in a Single-Mode Laser

Defining the quantity K as the signal-to-noise ratio （SNR） and the normalized intensity fluctuation C（O） of a single-mode laser for bias signal modulation driven by color noises with colored correlation, the whole output properties of the laser system is described by K. It is found that there is a maximum in the curves of K versus D, Q, and io. The optimization parameters are gained.