【摘要】：船舶自動(dòng)化程度的提高對(duì)能源的需求也日益增長,而船舶的柴油機(jī)系統(tǒng)作為能源的主要來源,其重要性也越來越明顯。為提高柴油機(jī)的穩(wěn)定性能,降低故障發(fā)生率,本文提出一種基于分形技術(shù)和神經(jīng)網(wǎng)絡(luò)算法的故障診斷模型。該模型中的分形理論能夠甄別出故障的非線性特征,精確鎖定故障的來源;然后利用神經(jīng)網(wǎng)絡(luò)算法對(duì)柴油機(jī)故障的診斷進(jìn)行深度訓(xùn)練。最后利用LabVIEW仿真平臺(tái)和Matlab軟件進(jìn)行故障診斷能力仿真驗(yàn)證,本文提出的綜合診斷方法能夠有效識(shí)別故障來源和類型。
[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.
【作者單位】： 杭州科技職業(yè)技術(shù)學(xué)院機(jī)電工程學(xué)院;
【分類號(hào)】：U672
，

本文編號(hào)：2239012