Joint deep feature learning and unsupervised visual domain adaptation for cross-domain 3D object retrieval

With the widespread application of 3D capture devices, diverse 3D object datasets from different domains have emerged recently. Consequently, how to obtain the 3D objects from different domains is becoming a significant and challenging task. The existing approaches mainly focus on the task of retrieval from the identical dataset, which significantly constrains their implementation in real-world applications. This paper addresses the cross-domain object retrieval in an unsupervised manner, where the labels of samples from source domain are provided while the labels of samples from target domain are unknown. We propose a joint deep feature learning and visual domain adaptation method (Deep-VDA) to solve the cross-domain 3D object retrieval problem by the end-to-end learning. Specifically, benefiting from the advantages of deep learning networks, Deep-VDA employs MVCNN for deep feature extraction and domain alignment for unsupervised domain adaptation. The framework can enable the statistical and geometric shift between domains to be minimized in an unsupervised manner, which is accomplished by preserving both common and unique characteristics of each domain. Deep-VDA can improve the robustness of object features from different domains, which is important to maintain remarkable retrieval performance.     

Fault diagnosis and fault tolerant control for the non-Gaussian time-delayed stochastic distribution control system

The purpose of fault diagnosis of stochastic distribution control (SDC) systems is to use the measured input and the system output probability density functions (PDFs) to obtain the fault information of the SDC system. When the target PDF is known, the purpose of fault tolerant control of stochastic distribution control system is to make the output PDF still track the given distribution using the fault tolerant controller. However, in practice, time delay may exist in the data (or image) processing, the modeling and transmission phases. When time delay is not considered, the effectiveness of the fault detection, diagnosis and fault tolerant control of stochastic distribution systems will be reduced. In this paper, the rational square-root B-spline is used to approach the output probability density function. In order to diagnose the fault in the dynamic part of such systems, it is then followed by the novel design of a nonlinear neural network observer-based fault diagnosis algorithm. The time delay term will be deleted in the stability proof of the observation error dynamic system. Based on the fault diagnosis information, a new fault tolerant controller based on PI tracking control is designed to make the post-fault probability density function still track the given distribution, which is dependent of the time delay term. Finally, simulations for the particle distribution control problem are given to show the effectiveness of the proposed approach.     

基于Matlab的滚动轴承故障诊断方法的研究

滚动轴承被广泛应用于风力发电、直升机等各类机械设备中,由于其受到复杂的载荷作用并且工作环境较为恶劣,所以轴承较为容易受到损坏。如果不能及时发现轴承故障,则会造成较大的事故,或导致停产与造成经济上的损失。本文通过对轴承故障振动信号的采集,利用Matlab软件对数据进行处理,力求在初期就能够及时发现故障,为维修提供科学依据,节约维修时间和成本。     

Modelica扩展建模的故障诊断技术研究

故障诊断技术是提高故障检测和隔离能力,提高任务可靠性的重要手段,基于模型的故障诊断分析方法得到了广泛应用,但难以用于实时检测诊断,难以计算虚警率等设计指标。本文提出扩展多领域物理系统建模语言Modelica,将多种故障模态嵌入模型中,利用基于假设的真值维护系统(ATMS)方法分析了测试集的生成算法。理论上分析了计算虚警率的可行方案,体现了新模型的优势。     

基于多源用户标签的跨域兴趣融合模型研究

【目的/意义】通过对大数据环境下的多源用户兴趣特征有效融合,缓解个性化推荐中用户兴趣偏好数据的稀疏性和准确性问题。【方法/过程】考虑到多域的数据权威度、内容质量及体系结构的差异化较为明显,提出了基于多源用户标签的跨域兴趣融合模型,首先把多个域中的用户兴趣进行标签化处理,然后利用跨域用户识别和标签权重归一方法得到多个域的用户实体-标签矩阵,最后使用域权重影响系数对标签进行融合,构造具有复合权重的用户兴趣标签集。【结果/结论】使用5个来源数据域进行实验与分析,融合模型能够有效提高标签用户覆盖效果,在查全率不断提高的情况,融合域能够保持较高的标签用户查准率,有效提高用户兴趣特征的描绘效果。     

Ensemble unsupervised autoencoders and Gaussian mixture model for cyberattack detection

Previous studies have adopted unsupervised machine learning with dimension reduction functions for cyberattack detection, which are limited to performing robust anomaly detection with high-dimensional and sparse data. Most of them usually assume homogeneous parameters with a specific Gaussian distribution for each domain, ignoring the robust testing of data skewness. This paper proposes to use unsupervised ensemble autoencoders connected to the Gaussian mixture model (GMM) to adapt to multiple domains regardless of the skewness of each domain. In the hidden space of the ensemble autoencoder, the attention-based latent representation and reconstructed features of the minimum error are utilized. The expectation maximization (EM) algorithm is used to estimate the sample density in the GMM. When the estimated sample density exceeds the learning threshold obtained in the training phase, the sample is identified as an outlier related to an attack anomaly. Finally, the ensemble autoencoder and the GMM are jointly optimized, which transforms the optimization of objective function into a Lagrangian dual problem. Experiments conducted on three public data sets validate that the performance of the proposed model is significantly competitive with the selected anomaly detection baselines.     

Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes

Fault detection and diagnosis is crucial in recent industry sector to ensure safety and reliability, and improve the overall equipment efficiency. Moreover, fault detection and diagnosis based on k-nearest neighbor rule (FDD-kNN) has been effectively applied in industrial processes with characteristics such as multi-mode, non-linearity, and non-Gaussian distributed data. The main challenge associated with FDD-kNN is the on-line computational complexity and storage space that are needed for searching neighbors. To deal with these issues, this paper proposes a monitoring approach where the Fuzzy C-Means clustering technique is used to decrease the overall on-line computations and required storage by measuring the neighbors of the clusters’ centres rather than the raw data. After building the monitoring model off-line based on the data clusters’ centres, the faults are detected by comparing the average squared Euclidean distance between the on-line data sample and the clusters’ centres with a predefined threshold. Then, the detected faults can be diagnosed by calculating the contribution of each variable in the fault detection index. Furthermore, for easily analysing the fault diagnosis results, the relative contribution for each sample data vector is considered. A numerical example and the Tennessee Eastman chemical process are used to demonstrate the performance of the proposed FCM-kNN for fault detection and diagnosis.     

基于BP神经网络的变压器故障诊断

针对变压器故障征兆和故障类型的非线性特征,结合油中气体分析法,研究应用BP神经网络对变压器进行故障诊断。设计了一个基于BP神经网络的变压器故障诊断模型,通过仿真实验证明BP神经网络可以有效的运用到变压器故障诊断中。     

Parameter fault detection and estimation of a class of nonlinear systems using observers

The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method.     

Semisupervised SAR image change detection based on a siamese variational autoencoder

In synthetic aperture radar (SAR) image change detection, the deep learning has attracted increasingly more attention because the difference images (DIs) of traditional unsupervised technology are vulnerable to speckle noise. However, most of the existing deep networks do not constrain the distributional characteristics of the hidden space, which may affect the feature representation performance. This paper proposes a variational autoencoder (VAE) network with the siamese structure to detect changes in SAR images. The VAE encodes the input as a probability distribution in the hidden space to obtain regular hidden layer features with a good representation ability. Furthermore, subnetworks with the same parameters and structure can extract the spatial consistency features of the original image, which is conducive to the subsequent classification. The proposed method includes three main steps. First, the training samples are selected based on the false labels generated by a clustering algorithm. Then, we train the proposed model with the semisupervised learning strategy, including unsupervised feature learning and supervised network fine-tuning. Finally, input the original data instead of the DIs in the trained network to obtain the change detection results. The experimental results on four real SAR datasets show the effectiveness and robustness of the proposed method.     

D-S证据理论和多模型结合的模拟电路故障诊断

为提高模拟电路故障诊断的精确度和正确率,采用信息融合方法进行故障诊断。首先取不同频率下的输出增益作为特征参数,经ANFIS模型、BP模型、RBF模型3种方法的局部诊断,获得彼此独立的证据;然后采用D-S证据理论及方法对证据进行决策融合故障定位,并将局部诊断正确度加入到基本概率赋值的获取中。实例证明,经过融合处理后,诊断的可信度明显增加,有效地提高了故障诊断的正确率和精确度。     

A link-bridged topic model for cross-domain document classification

Transfer learning utilizes labeled data available from some related domain (source domain) for achieving effective knowledge transformation to the target domain. However, most state-of-the-art cross-domain classification methods treat documents as plain text and ignore the hyperlink (or citation) relationship existing among the documents. In this paper, we propose a novel cross-domain document classification approach called Link-Bridged Topic model (LBT). LBT consists of two key steps. Firstly, LBT utilizes an auxiliary link network to discover the direct or indirect co-citation relationship among documents by embedding the background knowledge into a graph kernel. The mined co-citation relationship is leveraged to bridge the gap across different domains. Secondly, LBT simultaneously combines the content information and link structures into a unified latent topic model. The model is based on an assumption that the documents of source and target domains share some common topics from the point of view of both content information and link structure. By mapping both domains data into the latent topic spaces, LBT encodes the knowledge about domain commonality and difference as the shared topics with associated differential probabilities. The learned latent topics must be consistent with the source and target data, as well as content and link statistics. Then the shared topics act as the bridge to facilitate knowledge transfer from the source to the target domains. Experiments on different types of datasets show that our algorithm significantly improves the generalization performance of cross-domain document classification.     

Fault reconstruction using a LPV sliding mode observer for a class of LPV systems

This paper proposes a new sliding mode observer for fault reconstruction, applicable for a class of linear parameter varying (LPV) systems. Observer schemes for actuator and sensor fault reconstruction are presented. For the actuator fault reconstruction scheme, a virtual system comprising the system matrix and a fixed input distribution matrix is used for the design of the observer. The fixed input distribution matrix is instrumental in simplifying the synthesis procedure to create the observer gains to ensure a stable closed-loop reduced order sliding motion. The ‘output error injection signals’ from the observer are used as the basis for reconstructing the fault signals. For the sensor fault observer design, augmenting the LPV system with a filtered version of the faulty measurements allows the sensor fault reconstruction problem to be posed as an actuator fault reconstruction scenario. Simulation tests based on a high-fidelity nonlinear model of a transport aircraft have been used to demonstrate the proposed actuator and sensor FDI schemes. The simulation results show their efficacy.     

面向核心基础零部件研发的技术知识转化模型构建研究——以新型高承载推力轴承为例

[目的/意义]技术知识作为核心基础零部件研发的重要资源,研究其转化过程利于提高企业知识资源配置效率,实现创新技术引领与跨领域发展。[方法/过程]从技术知识转化视角出发,结合多年的工程实践与理论研究,利用系统分析、知识挖掘、知识溯源与节点评审等方法和工具,提出一种面向核心基础零部件研发的技术知识转化模型。[结果/结论]借助模型研制出的新型高承载推力轴承,为工程机械企业在加快技术创新和产品升级方面提供了可资借鉴的思路。     

一种基于活动围道的纹理图像分割方法

本文将Gabor滤波器和各向异性扩散方程相结合，提出了一种基于活动围道的无监督纹理图像分割算法。采用基于总变分流的扩散函数，各向异性扩散方程可以有效地在保留纹理图像大尺度边界信息的同时对图像纹理区域进行平滑，获得比原始图像更易分割的简化图像。但是平滑过程中纹理信息的丧失，限制了该方法的通用性和有效性。为了在利用各向异性扩散方法的同时有效地提取和利用纹理信息，我们利用Gabor滤波器提取一组表征纹理方向性和尺度性的特征图像，同时将原始图像作为表征纹理灰度信息的一个特征通道考虑。再利用矢量形式的各向异性扩散方程对特征图像进行边界保持的各向异性平滑。我们将基于区域灰度统计参数估计的活动围道分割方法扩展到矢量空间，来对平滑后的纹理特征量进行分割。实验证明利用该纹理分割算法可以获得较好的效果。     

Improved similarity-based modeling for the classification of rotating-machine failures

Similarity-based modeling (SBM) is a technique whereby the normal operation of a system is modeled in order to detect faults by analyzing their similarity to the normal system states. First proposed around two decades ago, SBM has been successfully used for fault detection in varied systems. In spite of this success, there is not much study performed in the literature regarding its design, that encompasses both similarity metrics and model training. This work aims at contributing with an in-depth study of SBM for fault detection considering these two design aspects. This is done in the context of proposing a novel system to identify rotating-machinery faults based on SBM, that is employed either as a standalone classifier or to generate features for a random forest classifier. New approaches for training the model and new similarity metrics are investigated. Experimental results are shown for the recently developed Machinery Fault Database (MaFaulDa) that has an extensive set of sequences and fault types, and for the Case Western Reserve University (CWRU) bearing database. Results for both databases indicate that the proposed techniques increase the generalization power of the similarity model and of the associated classifier, achieving accuracies of 98.5% on MaFaulDa and 98.9% on CWRU database.     

跨领域搜索对共性技术溢出的双刃剑效应：产业联盟的放大作用

在协同研发的背景下,多技术知识主体参与、多技术知识资源融合成为共性技术研发和溢出的主流范式,揭示跨领域搜索、产业联盟和共性技术溢出之间的关系,是现实驱动的结果。本文针对当前研究对“共性技术溢出如何产生”这一问题关注不足的现状,从企业“搜索行为”出发,基于专利分析法和内容分析法,构建了373家企业专利数据样本和190家企业产业联盟数据样本,通过实证研究发现：跨领域搜索对共性技术溢出的影响存在双刃剑效应,而产业联盟特性在二者关系中起到放大作用。主要研究结论是：（1）跨领域搜索与共性技术溢出深度存在倒U型关系;（2）跨领域搜索与共性技术溢出广度存在倒U型关系;（3）产业联盟的数量在“跨领域搜索-共性技术溢出深度”“跨领域搜索-共性技术溢出广度”的关系中均存在正向调节作用;（4）产业联盟的异质性在“跨领域搜索-共性技术溢出深度”“跨领域搜索-共性技术溢出广度”的关系中均存在正向调节作用。本文在一定程度上向前延伸了共性技术溢出的前因研究,并为企业和政府提供一定的现实借鉴意义。     

Active fault tolerant control of wind turbine systems based on DFIG with actuator fault and disturbance using Takagi–Sugeno fuzzy model

This paper presents an active fault tolerant control (FTC) for doubly fed induction generator (DFIG) with actuator fault and disturbance using Takagi–Sugeno (TS) fuzzy model. The control structure has two parts: fault and disturbance estimation part and FTC part. First, a TS fuzzy model is used to describe the DFIG system. Using a special linear transformation, the original system is decoupled into three independent subsystems: state subsystem without fault and disturbance, disturbance subsystem without fault, and fault subsystem without disturbance. By solving linear matrix inequalities (LMIs), a TS fuzzy observer is designed for the state subsystem without fault and disturbance. Second, estimations of faults and disturbance are obtained using the other subsystem models. Third, an active FTC scheme is developed to reduce the effect of disturbance and actuator faults. Finally, the performance of the proposed FTC is tested for a wind turbine system based on DFIG with actuator faults and disturbance. The simulation results demonstrate that the new FTC scheme makes possible to obtain an efficient fault and disturbance estimation and to reduce the peak current in the transient process.     

Fault diagnosis for processes with feedback control loops by shifted output sampling approach

Data-driven fault diagnosis of closed loop processes has been a challenge in the process control community. The issue of the interaction between the process model and the controller model exists in models directly identified from closed loop data, because for all the measured process outputs, no matter whether they are normal or faulty, they are fed back into the controllers so that the reconstruction-based contribution (RBC) as the fault diagnosis method has a severe fault smearing effect. This article proposes a novel sampling scheme which can significantly eliminate the adverse effect of modeling issues in feedback control. The identifiability condition of model parameters is satisfied in the new sampling framework so that the RBC recovers its efficiency even though the process runs under feedback control. Two benchmarks, a continuous stirred-tank heater process and the Tennessee Eastman challenge problem, are used to test the efficiency of the proposed method.     

支持向量机在分类问题中的应用研究

支持向量机是一种基于统计学习理论的机器学习方法,针对小样本情况表现出了优良的性能,目前被广泛应用于模式识别、函数回归、故障诊断等方面。这里主要研究支持向量机分类问题,着重讨论了以下几个方面的内容。首先介绍了支持向量机分类器算法,并将其应用于数据分类,取得了较高的准确率,所用数据来自于UCI数据集。仿真结果表明该算法具有较快的收敛速度和较高的计算精度。