Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.     

Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis

This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.     

地震动力: 由巨观到微观——1999年台湾集集地震

决定地震发生时的断层破裂能量和了解一个断层带的孕育与发展都需要地震和地质数据的结合.在一个大地震的发生过程中,藉由地震仪的记录分析,了解地震断层破裂过程中其断层的几何破裂行为及其机制,甚至分析其运动学上的灾害行为.在地震源分析中文章作者将以上行为分析称为地震的巨观分析.而地震的微观分析,则是以探讨当地震断层及破裂前缘持续向前前进时,其所需的破碎能量及其形成的极小颗粒之断层泥的物理化学机制.此断层滑移带中的断层泥之物理机制、化学组成及地震断层滑移带厚度,皆为了解地震滑移时摩擦行为及能量释放的重要参数.地震的巨观及微观行为的结合分析,为地震学上重要的突破,使人们得以进一步了解地震破裂过程中的摩擦行为、温度及压力的变化,并探讨地震时造成的地表位移、速度及加速度行为.但断层滑移带的断层泥并不易获得,除非有清楚的深部断层几何,并能以深钻的方式取得断层泥材料进行分析.1999年7.6级的台湾集集大地震产生地表或近地表8—12m的滑移,此近地表的滑移是钻井容易达成的,因此提供一次难得的机会,得出大地震滑移带的断层泥了解大滑移断层的动力机制.而2008年四川汶川地震为另一了解此巨观与微观机制的地震.     

基于模糊产生式规则的企业供电网谐振故障诊断研究与仿真

针对大型企业供配电网随机谐振故障问题进行分析研究。建立了某企业供电网铁磁谐振非线性框图模型,仿真表明系统会产生铁磁谐振。由于谐振表现形式具有一定的模糊性,引入模糊方法,建立了故障征兆的模糊隶属度函数。提出了一种故障诊断的模糊表达式,及基于模糊产生式规则的故障     

复杂机载电子系统故障综合诊断技术研究

故障综合诊断技术一直是复杂机载电子系统研发过程中的关键部分,当前的故障诊断技术同时需要机内测试（BIT）和场外自动化测试设备（ATE）的测试结果才能得出诊断结果,诊断效率低,时间长并且不能在线诊断。针对新一代战斗机将更加依赖航空电子系统的趋势,迫切需要一种诊断时间短,且能够实现在线诊断的故障诊断技术。因此,一种基于模型的故障诊断方法被提出。该方法通过融合多信号模型和整数编码故障字典模型,模块间采用多信号模型,单个模块中采用整数编码故障字典模型,克服了多信号模型对测试信息的浪费和整数编码故障字典模型建模困难的缺点,并提出一种多目标测试优选方法,通过优化检测方案,充分发挥BIT的检测性能。该方法通过充分使用BIT的测试信息,摆脱了对场外ATE的依赖,实现了在线快速定位故障并识别故障模式。     

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults’ classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components’ sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback–Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology’s performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs’ variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved 100% classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications.     

Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).     

某型相控阵雷达信号处理系统故障诊断系统设计与应用

针对某型相控阵雷达信号处理分系统故障部位难以定位,严重影响雷达工作能力这一难题,本文通过对故障现象进行分类,建立相应的故障匹配文件库,采用精确单模式串匹配算法,设计开发了一套故障诊断软件。该软件实现了故障部位准确定位功能,为快速、及时排除故障提供了技术支持,并在实际应用中验证了其正确性。     

Charge transfer instability in a mixed Ir/Rh honeycomb lattice in Li2Ir1−xRhxO3 solid solution

The solid solution series Li₂Ir_1-xRh_xO₃ is synthesized for several values of x between 0 and 1. The compounds possess a monoclinic layered structure (space group C2/m) throughout the solid solution range with the lattice constants following Vegard's relationship. Magnetization and resistivity data below room temperature are presented. The effective magnetic moment (μ_eff) is reduced below the value obtained by interpolating between the end-members, presumably due to nearest neighbor charge exchange leading to non-magnetic Ir⁵⁺/Rh³⁺ pairs. Surprisingly, the degree of reduction of μ_eff cannot be explained by a random mixture of Ir and Rh and, in particular, is strongly asymmetric around x = 0.5. This anomalous moment reduction possibly results from the difference in on-site Coulomb repulsion between Ir and Rh ions.     

列车防滑器主机故障诊断系统设计

SAB型列车防滑制动系统应用于国内T型列车,数量多,为了提高列车防滑器主机故障检测和维修效率,针对SAB WABCO公司的SWKAS20C型列车防滑器主机,设计了元件级故障检测与定位专家系统。通过研究模拟电子电路故障诊断技术,设计了基于支持向量机的多故障分类器,用于主机模拟电路故障样本数据的训练和测试,并通过改进算法寻优向量机参数,提高了故障诊断的准确度。搭建了模拟列车运行环境的测试平台,用以模拟主机在多种状态下的故障模式;使用VS2010开发环境编写了MFC专家系统。