某3级风扇试验流场诊断及性能优化

压气机试验是检验压气机设计是否达标的重要过程,并可在原设计基础上进行优化来发掘设计的潜能。利用级间总压和壁面静压在某3级风扇第1阶段试验对级间流场进行了详细测量和级性能诊断,提出了静叶角度优化方案;通过第2阶段试验,得出了角度优化后使设计转速喘振裕度提高了7.5个百分点,达到了设计指标的结论。流场诊断技术在压气机试验中起到了关键作用。结果表明:壁面静压结合级间径向总压测量方法基本能够满足在压气机试验中性能优化的需要。     

某型导弹翼轴花键齿首齿机加工精度研究

就如何保证新一代导弹重要零件翼轴花键齿首齿位置精度达到±１０’的机加工技术作了详细的阐述，着重就花键齿插齿过程中的工装定位、首齿位置精度的提高和测量方法进行了较为认真的研究。实践证明，该种插齿工艺方法能保证翼轴零件首齿位置精度的互换性，在具有类似的首齿位置精度要求的齿轮加工行业中有一定的先进性，并具有借鉴和推广价值。     

高血压鉴别诊断专家系统HEDDS的不精确推理算法

本文在分析著名的医学专家系统MYCIN不精确推理算法的基础上,提出了一种易于在微型计算机上实现的简便有效的不精确推理算法,并就其一些性质进行了讨论。     

相位信息在故障诊断中的应用

 在故障诊断中,人们多从频域中寻找特征,但对于转子系统,许多故障的特征是相似的。相位作为旋转机械里一个很实用的量,在故障诊断中一直没有得到很好的应用,本文指出的相位计算和测量概念和方法,旨在进一步识别故障。     

基于改进IRN网络的航空发动机故障诊断

建立了一种带偏差单元的内部回归神经网络.该网络的主要特点是记忆特性好,收敛速度快、稳定性强.介绍了该神经网络在航空发动机故障诊断中的应用方法,并通过实例验证了该方法在发动机故障分类中的实用性.在对某型航空发动机故障诊断中,准确性极高.从而在适时与视情维修过程中提出合理的建议,以便为相应维修方法的实施、改进和研究应用提供可靠的依据,有着良好的推广应用前景.     

基于Gauss核函数的SVM故障诊断技术研究

本文提出了基于Gauss核函数的SVM故障诊断技术,并利用grid法对SVM中参数进行寻优,以保证得到最高分类精度.通过对某航空公司ERJ145的右发AE3007发动机进行故障诊断研究后,证明基于Gauss核函数的SVM在小样本故障诊断中有着较好的适用性和可信性,能够为飞机发动机故障诊断提供有效的参考.     

基于DBN的不均衡样本驱动民航发动机故障诊断

在结合深度置信网络(DBN)、采样与集成技术的基础上,提出了基于不均衡样本驱动的民航发动机故障诊断模型。该模型通过分析民航发动机历史飞行数据,利用DBN提取性能参数中的内部特征,利用采样技术将不均衡样本均衡化,采用集成技术进行故障分类。将该模型应用到CFM56-7B系列发动机历史飞行数据,实验结果表明:与常用故障诊断方法相比,该模型的准确率高达0.996,AUC值高达0.948,可以有效处理民航发动机样本高维、不均衡问题。      

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.     

卷积神经网络和峭度在轴承故障诊断中的应用

针对传统智能诊断方法依靠专家知识和人工提取数据特征工作量大的问题,结合深度学习方法在特征提取和处理大数据方面的优势,研究了一种基于卷积神经网络和振动信号峭度指标的滚动轴承故障诊断方法。该方法将深度学习应用于轴承故障诊断,提取滚动轴承正常状态、内圈故障、外圈故障和滚动体故障4种状态的振动信号,将振动信号分段处理得到峭度指标,使用数据到图像的转换方法将峭度指标转换为灰度图,送入卷积神经网络模型完成故障分类。在进行滚动轴承故障诊断的实验时,所提的模型诊断准确率达到99.5%,高于传统支持向量机(SVM)算法的95.8%。