分形技术与概率神经网络在船舶柴油机故障诊断中的应用
发布时间：2018-09-12 12:29
【摘要】：船舶自动化程度的提高对能源的需求也日益增长,而船舶的柴油机系统作为能源的主要来源,其重要性也越来越明显。为提高柴油机的稳定性能,降低故障发生率,本文提出一种基于分形技术和神经网络算法的故障诊断模型。该模型中的分形理论能够甄别出故障的非线性特征,精确锁定故障的来源;然后利用神经网络算法对柴油机故障的诊断进行深度训练。最后利用LabVIEW仿真平台和Matlab软件进行故障诊断能力仿真验证,本文提出的综合诊断方法能够有效识别故障来源和类型。
[Abstract]:With the improvement of ship automation, the demand for energy is increasing, and the importance of marine diesel engine system as the main source of energy is becoming more and more obvious. In order to improve the stability of diesel engine and reduce the fault rate, this paper presents a fault diagnosis model based on fractal technology and neural network algorithm. The fractal theory in the model can identify the nonlinear characteristics of the fault and accurately lock the source of the fault, and then use the neural network algorithm to train the diesel engine fault diagnosis in depth. Finally, the LabVIEW simulation platform and Matlab software are used to verify the ability of fault diagnosis. The comprehensive diagnosis method proposed in this paper can effectively identify the source and type of fault.
【作者单位】： 杭州科技职业技术学院机电工程学院;
【分类号】：U672
，
本文编号：2239012
[Abstract]:With the improvement of ship automation, the demand for energy is increasing, and the importance of marine diesel engine system as the main source of energy is becoming more and more obvious. In order to improve the stability of diesel engine and reduce the fault rate, this paper presents a fault diagnosis model based on fractal technology and neural network algorithm. The fractal theory in the model can identify the nonlinear characteristics of the fault and accurately lock the source of the fault, and then use the neural network algorithm to train the diesel engine fault diagnosis in depth. Finally, the LabVIEW simulation platform and Matlab software are used to verify the ability of fault diagnosis. The comprehensive diagnosis method proposed in this paper can effectively identify the source and type of fault.
【作者单位】： 杭州科技职业技术学院机电工程学院;
【分类号】：U672
，
本文编号：2239012
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