多信息融合技术在船舶动力装置故障诊断中的应用

船舶动力装置工作过程中会产生大量多域故障信号,通过收集、挖掘隐藏的关联信号,可以解决船舶动力装置在故障诊断中面临的诊断时长问题.文章采用K-均值聚类算法(K-means)对数据进行聚类,聚类结果输入BP神经网络进行模型训练,并在此基础上,设计了主成分分析法(PCA)对模型进行优化.结果 显示,2种算法都能有效降低网络诊...

Application of multi-information fusion technology in fault diagnosis of ship power plant

A large number of multi-domain fault signals are generated during the operation of a ship power plant.The diagnosis time problem in the fault diagnosis of the ship power plant can be solved by digging and collecting hidden correlated signals.In this paper,the K-means clustering algorithm is employed to cluster the data,and the clustering result is input into the back-propagation(BP)neural network for model training.On this basis,a principal component analysis(PCA)method is designed to optimize the model.The results show that both the two algorithms can effectively reduce the network diagnosis time and that the algorithm optimized by PCA can improve the convergence speed and accuracy of neural network diagnosis more effectively,which means that PCA can provide feasible optimization schemes for intelligent fault diagnosis algorithms.