基于BP_Adaboost神经网络的船舶桨叶故障预警模型

Early ship fault warning model based on BP_Adaboost neural network

为了提高船舶桨叶故障识别率,构建了一种基于BP_Adaboost神经网络的船舶桨叶故障预警模型。该模型首先采用数字水听器采集二进制船舶桨叶静水噪声信号,并利用MATLAB编程将二进制信号转换成WAV音频信号,通过梅尔频率倒谱系数法(Mel-Frequency Cepstral Coefficient,MFCC)提取特征值,得到36维桨叶静水噪声信号,最后运用BP_Adaboost神经网络进行分类识别预警。实验结果表明,基于BP_Adaboost神经网络的船舶桨叶故障预警模型能够高效分类预警船舶桨叶故障,与BP神经网络识别率对比,分类识别预警率高达96%。

In order to improve the fault recognition rate of ship blades,a fault prediction model based on BP_Adaboost neural network is proposed in this paper. The model uses digital hydrophone acquisition to collect binary data of ship hydrostatic blade noise,and uses MATLAB to convert binary signal into WAV audio format. Then the model uses Mel-Frequency Cepstral Coefficient（ MFCC） feature extraction method to extract features and gets the water noise signal in the 36 dimension,and finally uses BP_Adaboost neural network to classify and warn the fault of ship blades. The experimental result shows that the fault prediction model based on BP_Adaboost neural network can effectively classify and predict the failure of the ship blades compared with the BP neural network. The classification and recognition warning rate is as high as 96%.