光电雷达电子部件的量子神经网络故障诊断算法

A Fault Diagnosis Algorithm for the Photovoltaic Radar Electronic Equipment Based on Quantum Neural Networks

针对电路故障诊断时,故障模式之间存在交叉数据的模式识别问题,将多层激励函数的量子神经网络引入多传感器信息融合之中,提出一种基于量子神经网络的多传感器信息融合集成电路故障诊断算法.并将其应用到光电雷达电子设备故障诊断中,通过测试电子电路中被诊断元件的工作温度和工作电压两个物理量,求出两传感器对各待诊断元件的故障隶属度,利用多层激励函数的量子神经网络进行信息融合,得到融合的各待诊断元件的故障隶属度,从而确定故障元件,提高故障诊断的准确率.

An information fusion algorithm based on the quantum neural networks for the pattern recognition with overlapping classes is presented, and it is used in the photovoltaic radar electronic equipment fault diagnosis. By measuring the temperature and voltage of circuit component, the membership functional assignment of two sensors to circuit component is calculated, and the fusion membership functional assignment is gained by using the multi-level transfer function quantum neural networks（QNN） ,then according to the fusion data, the fault component is found. Comparing the diagnosis results based on separate original data with the ones based on QNN fused data,it is shown that the quantum fusion fault diagnosis method is more accurate.