考虑状态约束的弹性高超声速飞行器自适应饱和容错控制

针对弹性高超声速飞行器跟踪控制问题进行研究,主要考虑状态约束、输入饱和、执行器故障等约束条件.首先,将弹性模态、外界干扰和模型参数不确定性归结为一类带有未知上界的系统干扰.其次,根据被动容错思想,针对速度和高度子系统分别设计抗饱和自适应容错控制器,通过引入双曲正切函数和新型屏障李雅普诺夫函数分别处理了输入饱和与状态约束...     

基于AI+神经网络的数字化故障电弧识别方案研究

线路故障电弧的特征随着电力电子技术的提高更趋复杂，传统的故障电弧识别方式已无法应对新型用电系统。对比传统故障电弧识别方案，研究故障电弧产生类型和新型检测技术，提出基于谐波因素、总谐波畸变率、电流零休时间、电流变化速率、电流周期性等典型时域、频域特征的AI+神经网络的数字化故障电弧识别方案。该方案运用回归算法向量机对特征图进行分类处理，通过正向传输运算、反向传输运算和迭代回归运算三个步骤执行卷积神经网络，频繁迭代回归以获得最佳特征识别图。本文以调光器的故障电弧识别为例，对故障识别方案进行验证，结果表明采用此方案可实现更高效、更精准、更稳定地识别故障电弧。     

Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey

In practical mechanical fault detection and diagnosis, it is difficult and expensive to collect enough large-scale supervised data to train deep networks. Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task, which performs well on small data and reduces the demand for high computation power. However, the detection performance is significantly reduced by the direct transfer due to the domain difference. Domain adaptation (DA) can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data. In this survey, we review various current DA strategies combined with deep learning (DL) and analyze the principles, advantages, and disadvantages of each method. We also summarize the application of DA combined with DL in the field of fault diagnosis. This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.     

New findings and appraisal of structural control on polygonal impact crater rim-geometry,a study from Mare Fecunditatis,Moon

Polygonal Impact Craters (PICs), having a distinct polygonal rim geometry, are common on terrestrial planets, their natural satellites such as Earth’s Moon and the asteroids. The straight segments of PIC-rims are arguably subparallel or oblique to existing fracture/fault planes in their vicinity, and such pre-existing structural weak planes are considered responsible for the shape of the PICs. The Mare Fecunditatis, a lunar maria, preserves mappable PICs as well as different geomorphic features like wrinkle ridges, grabens and pit crater chains which owe their origin to either compressional or extensional faulting. To understand the structural control, if any, on the PIC-rim geometry in Mare Fecunditatis, PICs, both simple and complex, and the deformational features are mapped, superposition relations between them are observed and trends are compared. The comparison between frequency of rim segment trends of the two types of PICs with wrinkle ridges, grabens and pit crater chains, and also statistical correlation between them, interestingly indicate that the wrinkle ridges and grabens are found to have negligible or no influence on the rim geometry of the PICs. Wrinkle ridges, known to be a group of the Mare Fecunditatis' oldest deformation features, are older than most of the preserved craters and are likely to have had control over the PIC shape. However, lack of correlation between the trends of wrinkle ridges and PIC rims indicates that most of the craters were formed after the fractures beneath the old wrinkle ridges ceased to act as mechanical discontinuity planes due to possible induration caused by fracture-filling through concomitant and later magma injection. The PICs dispersed throughout the maria could have avoided the influence of the grabens, the majority of which are located near the margin of the maria. Pit crater chains with possible deep roots and still/recently continued dike activities, were the only available weak planes which could influence the PIC rim shapes.     

Continual learning fault diagnosis:A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments

As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments....     

基于矢量化检测联邦滤波的INS/BDS/地磁组合导航容错方法

组合导航能够将多种类型的导航信息进行结合,实现优势互补,因此成为了目前导航应用领域的主要发展方向。然而,导航信息的增多势必会引入更多的风险源,从而降低导航系统的可靠性。基于联邦滤波的容错方法是目前抑制故障信息影响的主要解决手段,但是现有的故障容错方法普遍采用统一的检测机制,没有根据各个导航子系统的误差传播特性针对性地构建故障检测模型,因此会引起较高的误警率与漏检率。针对上述问题,提出了基于矢量化检测联邦滤波的INS/BDS/地磁组合导航容错方法。通过构建面向INS/BDS/地磁不同导航信息的故障检测函数,能够实现更加准确的矢量化信息分配,从而可以有效避免可用导航信息的损失以及故障导航信息对整体系统的影响。仿真结果表明,提出的方法可以有效隔离不同类型的故障信息,并减小其对无故障导航信息及整体系统的影响,从而提高了组合导航的精度和可靠性。     

An effective crack position diagnosis method for the hollow shaft rotor system based on the convolutional neural network and deep metric learning

In recent years, the crack fault is one of the most common faults in the rotor system and it is still a challenge for crack position diagnosis in the hollow shaft rotor system. In this paper, a method based on the Convolutional Neural Network and deep metric learning (CNN-C) is proposed to effectively identify the crack position for a hollow shaft rotor system. Center-loss function is used to enhance the performance of neural network. Main contributions include: Firstly, the dynamic response of the dual-disks hollow shaft rotor system is obtained. The analysis results show that the crack will cause super-harmonic resonance, and the peak value of it is closely related to the position and depth of the crack. In addition, the amplitude near the non-resonant region also has relationship with the crack parameters. Secondly, we proposed an effective crack position diagnosis method which has the highest 99.04% recognition accuracy compared with other algorithms. Then, the influence of penalty factor on CNN-C performance is analyzed, which shows that too high penalty factor will lead to the decline of the neural network performance. Finally, the feature vectors are visualized via t-distributed Stochastic Neighbor Embedding (t-SNE). Naive Bayes classifier (NB) and K-Nearest Neighbor algorithm (KNN) are used to verify the validity of the feature vectors extracted by CNN-C. The results show that NB and KNN have more regular decision boundaries and higher recognition accuracy on the feature vectors data set extracted by CNN-C, indicating that the feature vectors extracted by CNN-C have great intra-class compactness and inter-class separability.     

A new interpretable fault diagnosis method based on belief rule base and probability table

It is vital to establish an interpretable fault diagnosis model for critical equipment. Belief Rule Base (BRB) is an interpretable expert system gradually applied in fault diagnosis. However, the expert knowledge cannot be utilized to establish the initial BRB accurately if there are multiple referential grades in different fault features. In addition, the interpretability of BRB-based fault diagnosis is destroyed in the optimization process, which reflects in two aspects: deviation from the initial expert judgment and over-optimization of parameters. To solve these problems, a new interpretable fault diagnosis model based on BRB and probability table, called the BRB-P, is proposed in this paper. Compared with the traditional BRB, the BRB-P constructed by the probability table is more accurate. Then, the interpretability constraints, i.e., the credibility of expert knowledge, the penalty factor and the rule-activation factor, are inserted into the projection covariance matrix adaption evolution strategy to maintain the interpretability of BRB-P. A case study of the aerospace relay is conducted to verify the effectiveness of the proposed method.     

Synchronous chirp mode extraction: A promising tool for fault diagnosis of rolling element bearings under varying speed conditions

As critical components in modern aerospace productions, rolling element bearings(REBs) generally work under varying speed conditions, which brings great challenges to their operating health monitoring. Some novel time–frequency decomposition(TFD) algorithms are established recently to extract nonlinear features from the non-stationary signals effectively, which are promising for realizing fault diagnosis of REBs under varying speed conditions. However, numerous personal experiences must be incor...     

基于解耦矢量控制的五相永磁同步电机容错控制策略

针对五相永磁同步电机两相开路故障的运行工况,提出了一种基于解耦矢量控制理论的故障容错控制策略.该策略通过在两相开路故障下的同步旋转坐标系内建立电机的数学模型,构造解耦变换矩阵,将电流控制中id和iq轴解耦,达到两相开路故障下容错运行的目的 .仿真结果表明,在五相永磁同步电机两相开路故障下,基于解耦矢量控制理论的故障容错控制策略能够实现电机驱动系统的高品质运行,具有控制简单、转矩波动小、容错能力强等优点.