基于故障敏感度的证据权重计算方法及其应用
The Calculating Method and Application of Evidence Weight Based on Fault Sensitivity

典型证据权重计算方法存在只有少数传感器判断正确而多数判断错误的高冲突证据的加权D-S决策融合问题，针对此问题，提出一种基于故障敏感度的证据权重计算方法。首先，通过核函数主元分析（kernel principal component analysis,简称KPCA）提取非线性的敏感特征；其次，基于故障检测原理计算该特征的故障敏感度，并将其作为传感器的故障敏感度；最后，计算得到基于故障敏感度的传感器决策权重，并将该权重及等权重法和基于决策距离方法的权重共同应用于转子故障模拟实验台的融合检测与诊断中。结果表明，该方法能对故障敏感、包含故障信息多的传感器赋予更高的权重值，提高其决策地位和作用，反之则赋予较小的权重，“弱化”其决策地位和作用。通过证据权重的“调节”作用，使得该方法论是在只有少数传感器发现故障的证据高冲突情况还是在冲突不大或冲突时，均取得了更好的决策融合结果。
With a focus on the problem of the typical evidence weight calculating method that it cannot define the weight and complete the decision fusion in the high conflict evidence D-S fusion problem where only a few sensors make the judgments correctly while the most incorrectly, the paper proposes an evidence weight calculating method based on fault sensitivity. Firstly, kernel principal component analysis (KPCA) is used to obtain the nonlinear sensitive feature; then, calculating the sensitivity based on such feature and obtaining sensor decision weight based the fault detection sensitivity; applying the weight obtained above and that obtained from equal weight method and decision-making distance based method to the fusion diagnosis of the simulated fault in the rotor where three sensor are installed. The results show that: the weight obtained through the method proposed in the paper can reflect the sensitivity of different sensors when detecting the faults. High weight is given to the sensors which contain much fault information and sensitive to fault and low weight will be given to the sensors of the opposite kind. In the method proposed, evidence weights play the role as a “regulator”, which makes it possible to obtain better decision fusion result whether in the cases where only a few sensors manage to find the fault and give the correct diagnosis or in the cases where few or no conflict exists.