两级信息融合技术在柴油机故障诊断中的应用

Application of Two-level Information Fusion Technology in the Diesel Engine Malfunctions Diagnosis

针对传统神经网络在故障诊断中因测点信息多而导致的网络庞大、收敛困难等问题,引入集成神经网络,提高了融合诊断效率;同时引入基于D-S证据理论,这种决策融合方法解决了集成神经网络各个子网诊断结果不一致的问题。在应用于柴油机故障诊断时,首先对测取的正常和故障样本进行小波包AR谱分析,同时提取各个特征频带的能量分别作为集成神经网络对应子网的输入进行诊断,当其无法确定诊断结果时,再运用证据理论进行决策融合输出最终诊断结果。试验证明:基于集成神经网络和D-S证据理论的两级综合诊断模型提高了诊断的准确性和可靠性。

Aiming at the disadvantages of the net being huge in the result of too much testing point information and converging being difficult of the traditional neural network, this paper proposes the composite neural network to enhance the efficiency of fusion diagnosis. At the same time, this paper proposes the D-S evidence theory which is a decision-making fusion method to solve the problem of inconsistent diagnosis result from each subnet. In the application of diesel engine malfunction diagnosis, firstly the wa...