为了提高船舶维护效率，提出一种多传感器融合下船舶机电系统多发故障信号监测方法。根据故障状态下的信号频率，使用小波变换法提取故障信号特征参数作为蚁群算法优化BP神经网络输入，实现多发故障诊断，并通过DS证据理论完成多传感器数据融合，得出故障诊断结果。实验结果表明，该方法可通过多传感器融合判断出船舶机电系统故障类型，即使一种传感器出现故障也不影响诊断效果，诊断船舶机电系统多发故障平均准确率高达97.02%，能够实现较为精准的船舶机电系统多发故障监测。
In order to improve the efficiency of ship maintenance, a multi-sensor fusion based monitoring method for multiple fault signals in ship electromechanical systems is proposed. Based on the frequency of the signal in the fault state, the wavelet transform method is used to extract the characteristic parameters of the fault signal as input for the ant colony algorithm to optimize the BP neural network, achieve multi fault diagnosis, and complete multi-sensor data fusion through DS evidence theory to obtain the fault diagnosis results, realizing the monitoring of multi fault signals in the ship's electromechanical system. The experimental results show that this method can determine the type of faults in ship electromechanical systems through multi-sensor fusion. Even if one sensor fails, it does not affect the diagnostic effect. The average accuracy of diagnosing multiple faults in ship electromechanical systems is as high as 97.02%, which can achieve more accurate monitoring of multiple faults in ship electromechanical systems.
2024,46(5): 149-152 收稿日期：2023-12-01
DOI：10.3404/j.issn.1672-7649.2024.05.027
分类号：TP277
作者简介：李烈熊(1984-),男,硕士,讲师,研究方向为机电系统辨识及故障监测
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