基于相关向量机的反应堆功率测量电路故障预测研究

为了提高核测量装置的保障性和维修性，本文以反应堆功率测量放大电路为对象，通过基于量子粒子群优化算法的多核相关向量机模型对电路的典型故障进行预测。从功率测量放大电路的脉冲响应信号中，用小波包分解方法提取特征信息，将计算所得到的特征与电路正常状态特征之间的欧氏距离作为电路故障程度指标，选用多核相关向量机建立电路故障预测模型，并分析了相关向量机模型核函数种类、参数优化算法对于模型预测效果的影响，研究结果表明采用量子粒子群算法优化的多核相关向量机模型对于电路未来运行状态的预测精度较优，能够准确预测电路故障程度的变化规律。 

Research on Fault Prediction of Reactor Power Measurement Circuit Based on Relevance Vector Machine

In order to improve the supportability and maintainability of the nuclear measuring device, taking the reactor power measurement amplifier circuit as the object, this paper predicts the typical faults of the circuit through the multi-kernel relevance vector machine model based on quantum particle swarm optimization algorithm. From the pulse response signal of the power measurement amplifier circuit, the feature information is extracted by the wavelet packet decomposition method, and the Euclidean distance between the feature and the normal state feature of the circuit is calculated as the fault degree indicator of the circuit. Multi-kernel relevance vector machine is selected to establish circuit fault prediction model. The influence of kernel function type and parameter optimization algorithm of relevance vector machine model on the prediction effect of the model is analyzed. The research results show that the multi-kernel relevance vector machine model optimized by quantum particle swarm algorithm has better prediction accuracy for the future running state of the circuit, and can accurately predict the changing law of the fault degree of the circuit.