Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.     

Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

A multi-stage regulation strategy of space manipulators with a free-swinging joint failure

To ensure tasks can be completed after a free-swinging joint failure occurs, a multi-stage regulation strategy of space manipulators is proposed. Considering all terms of the dynamics equation, an evaluation model of the regulation ability (EMRA) of active joints over the fault joint is established based on the fuzzy entropy. And then a multi-stage regulation strategy based on the EMRA is designed to regulate the fault joint. The strategy divides the regulation process into several stages, and select a certain active joint to regulative the fault joint in every stage. With this multi-stage regulation strategy, the fault joint can be regulated to the desired angle without huge torque on regulative joints. The simulation is carried out with a 7-DOF space manipulator, verifying the correctness and effectiveness of the multi-stage regulation strategy. The strategy has three advantages: Coriolis and centrifugal terms are both considered for the first time in selecting the regulative joint, making the selection result more in line with the actual regulation process; The influence of the model uncertainty is eliminated in establishing the EMRA, making the evaluation of regulative ability more precise; The fault joint is successfully regulated to the desired angle without huge torque on regulative joints.     

基于卷积门控循环网络的滚动轴承故障诊断

针对许多基于深度学习的滚动轴承故障诊断方法在小样本数据集下诊断性能下降的问题,提出一种基于卷积门控循环神经网络的轴承故障诊断模型。该模型使用两层的卷积网络来从输入信号中提取特征,同时使用tanh函数作为激活函数,且池化层使用大池化核来进行重叠下采样。将所提取得到的高层特征连接到双向门控循环网络。合并循环网络正向和逆向的最后一个状态,并连接一层全连接层进行输出。选用凯斯西储大学的轴承故障数据集来验证模型在小样本数据集下的诊断性能,实验结果表明,相比于其他类型的模型,该模型在仅有20个训练样本的情况下依然保持97%的识别准确率。      

AP聚类改进免疫算法用于航空发动机故障诊断

在免疫算法训练过程中引入近邻传播(AP)聚类与熵权法,对训练样本进行聚类与权值计算,将权值引入免疫算法中样本选择阈值的计算,以解决训练过程采用固定选择阈值所造成的检测器在部分区域过拟合,部分区域欠拟合的问题。结果表明:改进的免疫算法用于典型非线性函数的寻优时,迭代性能均优于传统免疫算法,并在大部分情况下优于粒子群算法与量子遗传算法,在进行某型发动机故障诊断的实例实验时,改进后的算法的诊断准确率达到98.06%,高于传统免疫算法的92.60%。      

FDI飞机舵面损伤故障全局检测的非线性数学模型

阐述了飞机在单舵面损伤故障下 ,建立故障状态下的数学模型 ,以及同样输入条件下飞机正常运动时的数学模型 ,得到飞机在相同飞行条件下损伤时的气动系数和无损伤正常时的气动系数 ,最终得到气动系数的残差。这将为以后飞机过载残差和角加速度残差奠定基础。论文中结合一定的算例给予证实推论的正确性。     

基于关联分析的机载电子设备SRU级故障诊断方法

针对机载设备二类检测设备将故障隔离到SRU的要求,对LRU的内部结构进行了分析,提出了点、底项、组底项的概念,用关联值表示点与点之间、点与SRU之间及组底项之间的关联关系;针对多SRU结构UUT,提出逐级排除法故障诊断方法.     

一种用于人体早期病变诊断的仪器

根据探针在人体穴位电流量的变化情况 ,具体分析了该仪器电路设计的工作原理、电路结构及具体的使用方法。     

现场动平衡技术在生产中的应用

利用美国恩泰克公司提供的DP1500数据采集仪及现场动平衡软件,以实例说明了现场动平衡技术在锅炉引风机、排粉风机、电动机、机泵等转动设备不平衡故障排除中的应用,介绍了不平衡故障的识别方法,揭示出该技术在生产中的实用性、先进性和经济性.     

基于磁场映像的印制电路板故障诊断方法研究

简要分析了印制电路板的电磁场，提出了用电磁感应法测量磁场的电咱故障诊断新方法，并给出了测试系统组成方案。最后通过验证实验证明了该方法是确实可行的。