共查询到18条相似文献,搜索用时 171 毫秒
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基于神经网络的航空发动机滑油监测分析 总被引:6,自引:2,他引:4
提出了一种基于BP神经网络的航空发动机滑油金属含量预测方法,给出了运用自回归模型(AR模型)预测模型和神经网络进行预测的一般公式。将其应用于某型发动机滑油的铁金属含量预测,结果表明,与传统的AR预测模型相比,神经网络表现出优秀的推广能力。经过数值仿真得出AR模型仅能预测出序列的变化趋势;神经网络预测推广能力强、具有较强的鲁棒性和容错性,可以为发动机的监控提供重要的依据。 相似文献
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基于支持向量机的航空发动机磨损趋势预测 总被引:1,自引:0,他引:1
基于支持向量机开发的航空发动机磨损趋势预测技术运用结构风险最小化准则,可通过内积函数将低维空间的非线性问题转化为高维空间的线性问题,在发动机滑油光谱监控中十分有用.阐述了支持向量机的原理和数学模型,建立了适用于航空发动机磨损趋势预测的支持向量机回归模型和自回归模型,并对支持向量的核函数模型参数进行了讨论.对实际发动机的润滑油光谱监控数据趋势预测结果表明,基于支持向量机回归模型的趋势预测技术具有很高的预测精度和很强的实用性,可有效提高通过润滑油光谱监控技术预报航空发动机磨损类故障的预测能力. 相似文献
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基于过程支持向量机的航空发动机润滑油消耗率预测 总被引:1,自引:1,他引:0
分析了目前航空发动机润滑油消耗率监控现状及其不足,提出了一种基于过程支持向量机的润滑油消耗率预测方法,针对润滑油消耗率实际数据有较大波动性的特点,使用了样条插值增加样本点的方法来提高预测精度。通过润滑油消耗率预测实例对提出的方法进行了检验,实例结果表明过程支持向量机能够比较准确地对润滑油消耗率进行预测。 相似文献
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针对航空发动机滑油系统状态监测问题,提出了递归过程神经网络模型。其隐层和输出层为过程神经元,该网络的输入信号为时变函数或过程,并且含有一个特别的关联层,在建模过程中能储存系统过去更多时刻的状态信息,使得网络结构适于预测时间序列问题。文中给出了相应的学习算法,并且分别利用人工神经网络和递归过程神经网络对航空发动机滑油系统状态进行预测。结果表明,递归过程神经网络预测精度高,优于传统人工神经网络的预测能力。为航空发动机滑油系统状态监测问题提供了一种有效的方法。 相似文献
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为研究航空发动机轴承腔油气两相流动特性,提高轴承腔回油特性,针对轴承腔的通风结构提出嵌入改进方案;建立基于欧拉-欧拉方法轴承腔两相流求解模型,对不同工况下常规轴承腔和嵌入改进方案轴承腔流动特性和回油特性进行分析。研究结果表明,将常规轴承腔通风结构进行嵌入改进后,润滑油被嵌入的通风口壁面阻挡,在空气剪切力和重力的作用下,通风口右侧的润滑油掠过通风口向下游移动,从通风口流出的润滑油量减小,从回油口流出的润滑油增加,因而使得回油效率明显提升;随着嵌入深度的增加,从通风口流出的润滑油得到进一步抑制,腔内润滑油体积分数进一步增加,回油效率得到进一步提升;相比常规轴承腔,当润滑油流量为200 L/h,转速为15 000 r/min时,嵌入改进方案回油效率提升最为明显,嵌入深度为8、10、12 mm的改进方案回油效率分别提高了16.72%、18.80%和20.19%。 相似文献
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Enhancement of oil debris sensor capability by reliable debris signature extraction via wavelet domain target and interference signal tracking 总被引:1,自引:0,他引:1
On-line oil debris monitoring is an effective approach to detecting machine component wear through estimating the size and the quantity of metallic debris in the lubricating oil. However, oil debris (particle) signatures are often contaminated by background noise and vibration interference during the operation of the oil debris sensor. As such, the accuracy of debris measurement and counting depends largely on the authenticity of the extracted debris signature. Considering characteristics of both target and interference signals obtained by the oil debris sensor, we propose a novel debris signature extraction technique to improve the oil debris measurement capability based on the wavelet domain information. In each wavelet scale of the oil debris sensor output signal, the debris coefficients are detected based on the singularity of the debris signal. The interference coefficients are estimated by adaptive linear prediction. The overlapped debris and interference coefficients are separated by a new prediction strategy involving alternating applications of forward and backward predictors. The differences between the mixture and the estimated interference coefficients are employed to reconstruct the debris signature. The proposed technique is evaluated using both uni- and bi-excitation experimental data and compared with a recently reported method. The experimental results and comparisons indicate that the proposed new method can extract the debris signature more truthfully, and thus improve the oil debris monitoring accuracy in real applications. 相似文献
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以某航空发动机附件齿轮箱“连通折弯式”的复杂油路为研究对象,对油路内部的三维流场进行仿真分析并建立压力-流量模型。基于流场的计算结果,分析齿轮箱进口润滑油流量不足的原因并提出优化方案。结果表明:该发动机因喷嘴结构设计不当,导致齿轮箱油路中局部流通面积较小,局部阻力损失较大,使得油路进口处的润滑油流量偏小。通过对喷嘴结构局部优化,提高了油路中局部流通面积,有效增加了进口的润滑油流量,满足了设计要求。优化后的齿轮箱油路中,压力损失最大的区域在每个喷嘴的喷孔段,但各个管流段压力变化不大,整个油路的压力分布更加合理。建立齿轮箱工作压力范围内的压力-流量的数学模型,为不同进口压力下的润滑油体积流量选择提供了数据支撑 相似文献
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The performance and particulate emission of a diesel engine are affected by the consumption of lubricating oil.Most studies on oil consumption mechanism of the cylinder have been done by using the experimental method,however they are very costly.Therefore,it is very necessary to study oil consumption mechanism of the cylinder and obtain the accurate results by the calculation method.Firstly,four main modes of lubricating oil consumption in cylinder are analyzed and then the oil consumption rate under common working conditions are calculated for the four modes based on an engine.Then,the factors that affect the lubricating oil consumption such as working conditions,the second ring closed gap,the elastic force of the piston rings are also investigated for the four modes.The calculation results show that most of the lubricating oil is consumed by evaporation on the liner surface.Besides,there are three other findings:(1) The oil evaporation from the liner is determined by the working condition of an engine;(2) The increase of the ring closed gap reduces the oil blow through the top ring end gap but increases blow-by;(3) With the increase of the elastic force of the ring,both the left oil film thickness and the oil throw-off at the top ring decrease.The oil scraping of the piston top edge is consequently reduced while the friction loss between the rings and the liner increases.A neural network prediction model of the lubricating oil consumption in cylinder is established based on the BP neural network theory,and then the model is trained and validated.The main piston rings parameters which affect the oil consumption are optimized by using the BP neural network prediction model and the prediction accuracy of this BP neural network is within 8%,which is acceptable for normal engineering applications.The oil consumption is also measured experimentally.The relative errors of the calculated and experimental values are less than 10%,verifying the validity of the simulation results.Applying the established simulation model and the validated BP network model is able to generate numerical results with sufficient accuracy,which significantly reduces experimental work and provides guidance for the optimal design of the piston rings diesel engines. 相似文献
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为了提高航空发动机滑油系统故障诊断的有效性,提出了一种基于遗传编程的故障特征提取模型。该模型首先利用遗传编程从原始特征集中提取更能反映故障本质的复合特征,然后通过Fisher判别分析进行二次特征提取,得到对分类识别最有效、数目最少的特征。在神经网络的分类试验中,经过遗传编程和Fisher判别分析提取的特征使样本集的可分性增大,分类正确率从80%左右提高到了97%以上,并且对分类器具有较强的鲁棒性,表明该模型提取的特征对滑油系统的几种典型故障具有更好的识别能力。 相似文献