Evaluation of fault diagnosability for nonlinear uncertain systems with multiple faults occurring simultaneously

Up to present,the problem of the evaluation of fault diagnosability for nonlinear systems has been investigated by many researchers.However,no attempt has been done to evaluate the diagnosability of multiple faults occurring simultaneously for nonlinear systems.This paper proposes a method based on differential geometry theories to solve this problem.Then the evaluation of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is achieved.To deal with the effect of control laws on the evaluation results of fault diagnosability,a design scheme of the evaluation of fault diagnosability is proposed.Then the influence of uncertainties on the evaluation results of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is analyzed.The numerical simulation results are obtained to show the effectiveness of the proposed evaluation scheme of fault diagnosability.

Application of Single Channel Blind Separation Algorithm Based on EEMD-PCA-RobustICA in Bearing Fault Diagnosis

Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals;a single channel blind source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the single-channel mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the overdetermined blind source separation conditions, the new matrix input RobustICA, to achieve the separation of the source signal. Finally, the isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately diagnose the location of mechanical fault.

Fault Diagnosing System of Steam Generator for Nuclear Power Plant Based on Fuzzy Neural Networks

All kinds of reasons are analysed in theory and a fault repository combined with local expert experiences is establishedaccording to the structure and the operation characteristic of steam generator in this paper. At the same time, Kohonen algo-rithm is used for fault diagnoses system based on fuzzy neural networks. Fuzzy arithmetic is inducted into neural networks tosolve uncertain diagnosis induced by uncertain knowledge. According to its self-association in the course of default diagnosis. thesystem is provided with non-supervise, self-organizing, self-learning, and has strong cluster ability and fast cluster velocity.

Fault diagnosis for t/(t+1)-diagnosable system based on the PMC model

In this paper, a t/(t+1)-diagnosable system is studied, which can locate a set S with |S|≤t+1 containing all faulty units only if the system has at most t faulty units. On the basis of the characterization of the t/(t+1)-diagnosable system, a necessary and sufficient condition is presented to judge whether a system is t/(t+1)-diagnosable. Meanwhile, this paper exposes some new and important properties of the t/(t+1)-diagnosable system to present the t/(t+1)-diagnosability of some networks. Furthermore, the following results for the t/(t+1)-diagnosability of some special networks are obtained: a hypercube network of n -dimensions is (3n-5)/(3n-4)-diagnosable, a star network of n -dimensions is (3n-5)/(3n-4)-diagnosable (n≥5) and a 2D-mesh (3D-mesh) with n 2(n 3) units is 8/9-diagnosable (11/12-diagnosable). This paper shows that in general, the t/(t+1)-diagnosability of a system is not only larger than its t/t -diagnosability , but also its classic diagnosability, specially the t/(t+1)-diagnosability of the hypercube network of n -dimensions is about 3 times as large as its classic t -diagnosability and about 1.5 times as large as its t/t -diagnosability.

Node Grouping in System-Level Fault Diagnosis

With the popularization of network applications and multiprocessor systems, dependability of systems has drawn considerable attention. This paper presents a new technique of node grouping for system-level fault diagnosis to simplify the complexity of large system di-agnosis. The technique transforms a complicated system to a group network, where each group may consist of many nodes that are either fault-free or faulty. It is proven that the transformation leads to a unique group network to ease system diagnosis. Then it studies systematically one-step t-faults diagnosis problem based on node grouping by means of the concept of hide-pendent point sets and gives a simple sufficient and necessary condition. The paper presents a diagnosis procedure for t-diagnosable systems. Furthermore, an efficient probabilistic diagnosis algorithm for practical applications is proposed based on the belief that most of the nodes in a system are fault-free. The result of software simulation shows that the probabilistic diagnosis provides high probability of correct diagnosis and low diagnosis cost, and is suitable for systems of any kind of topology.

Petri net model for diagnosis of permanent faults of a hydraulic system

Petri net model is applied to diagnose the permanent fault of hydraulic system within the framework of interpreted Petri net. The permanent fault is described as redundant structure of the model. A definition and a theorem are proposed to determine the diagnosability of the hydraulic system. The relations bwtween the diagnosability and other structure properties are also discussed. An example of actual hydraulic system is presented and its permanent fault can be diagnosed by the proposed method efficiently.

Analysis of superheater＇s pipe wall overtempemture by fault tree diagnose

After research on a 2000t/h subcritical forced-circulation balanced ventilation were applied boilerand the structure and operation d its auxillary system builds up thls heat transfer model of a superheater's pipe wall and analyze the effect of primary factors on the overtemperature of the pipe wall. Fault tree structure was used to uncover the multiplayer logic between the overternpemture of the superheater' s pipe wall and the faults.

Study on Method of Fault Vibration Diagnose for Roller Overrunning Clutch

Fault diagnose of the roller overrunning clutch is a headache problem in engineering at home and abroad. This paper introduces a new method to solve the problem by using the wavelet transform to separate fault si gnal and further analyzing the impact frequency. The signal local singularities under the wavelet transform are studied. According to the propagation features of modulus maximums of the fault signal and the noise under the wavelet transfor m different on the scales, and by use of the signal wavelet decomposition-recon struction algorithm, the clutch shell vibration acceleration signal is decompose d, denoised, and reconstructed.The signal-to-noise of the monitored signal imp roved greatly.The fault characteristic signal on time domain is positioned.The f ault characteristic frequency is picked up. Experiment shows that it is quite effective.

Intermittent Fault Diagnosability of Interconnection Networks

An interconnection network＇s diagnosability is an important metric for measuring its self-diagnostic capability. Permanent fault and intermittent fault are two different fault models that exist in an interconnection network. In this paper, we focus on the problem pertaining to the diagnosability of interconnection networks in an intermittent fault situation. First, we study a class of interconnection networks called crisp three-cycle networks, in which the Chin-number （the number of common vertices each pair of vertices share） is no more than one. Necessary and sufficient conditions are derived for the diagnosability of crisp three-cycle networks under the PMC （Preparata, Metze, and Chien） model. A simple check can show that many well-known intereonnection networks are crisp three-cycle networks. Second, we prove that an intereonnection network S is a ti-fault diagnosable system without repair if and only if its minimum in-degree is greater than ti under the BGM （Barsi, Grandoni, and Masetrini） model. Finally, we extend the necessary and sufficient conditions to determine whether an interconnection network S is ti-fault diagnosable without repair under the MM （Maeng and Malek） model from the permanent fault situation to the intermittent fault situation.

THEORY ON THE DIAGNOSABILITY OF THE FAULT CLASSIFICATION APPROACH

This paper describes, by means of a Voronoi hypersphere, the nearest neighbor relations of all the feature submatrices in the fault classification space and analyses the deviation disturbance angles between fault feature submatrices and a k-dimension unitary matrix of the measured voltage change matrix. With the above two concepts, this paper discusses the diagnos-ability in the fault classification approach. The paper also classifies and defines the fault diagnosis problems. Finally, the paper derives the corresponding necessary and sufficient conditions for correct location of faults.

Restricted-Faults Identification in Folded Hypercubes under the PMC Diagnostic Model

System-level fault identification is a key subject for maintaining the reliability of multiprocessor interconnected systems. This task requires fast and accurate inferences based on big volume of data, and the problem of fault identification in an unstructured graph has been proved to be NP-hard （non-deterministic polynomial-time hard）. In this paper, we adopt the PMC diagnostic model （first proposed by Preparata, Metze, and Chien） as the foundation of point-to-point probing technology, and a system contains only restricted-faults if every of its fault-free units has at least one fault-free neighbor. Under this condition we propose an efficient method of identifying restricted-faults in the folded hypercube, which is a promising alternative to the popular hypercube topology.

TOPOLOGICAL CONDITIONS FOR FAULT DIAGNOSABILITY AT SUBNETWORK-LEVEL

The fault analysis of analog circuits at subnetwork-level is confronted with two essentialcore problems: (ⅰ) Is it necessary that all the torn nodes are accessible? (ⅱ) Are there any topological conditions of subnetwork which guarantee the fault at subnetwork-level to bediagnosed correctly? This paper answers the two problems in two theorems. The conditions are necessary and almost sufficient, and if all the torn nodes are accessible, the required topological conditions can be almost satisfied automatically. This is a special situation of our research on the distribution of the accessible nodes.

基于DTW时序距离的故障可诊断性评估方法

对装备中具体故障进行可诊断性评估,可以量化地表明故障被诊断的难易程度。针对故障可诊断性评价方法中存在的不客观及度量方法选择不恰当的问题,提出一种基于动态时间规整(Dynamic Time Warping,DTW)的时序距离度量方法。定义可诊断性评估模型,从实际系统中抽取系统的结构、信号、测试、故障模式4种要素,为故障可诊断性评估工作的开展打下基础。摒弃传统的从信号中提取特征进行相似性度量的做法,将信号看做时序序列,基于DTW方法衡量不同状态下各信号之间的相似程度,作为故障可诊断性的评估依据。使用动量轮系统对新方法进行仿真实验,仿真结果表明新方法在进行可诊断性评估方面具有客观性和有效性。

笼型异步电机故障诊断实验台设计

异步电机是最常见的电气设备,在国民生产活动中占有重要的地位,电机故障诊断能够有效降低事故发生率和企业运行成本,对其进行研究有着重要的意义。为此以常用的笼型异步电机故障为对象,在完成电机及负载的选择、故障设置和电气系统设计的基础上,以电压、电流作为基本分析信号,完成了故障诊断实验台的开发。在该实验台上设置了气隙偏心、转子断条和匝间短路等三种故障,通过对输出电流信号进行了傅里叶变换和Park变换,对其故障特征进行了对比分析。结果表明,当电机存在气隙偏心故障时,在25Hz及75Hz频率点附近出现特征分量,当存在转子断条故障时,分别在频率为44.9Hz、45.9Hz、54.1Hz及55.1Hz处出现特征分量,当存在匝间短路故障时,Park轨迹图的椭圆形扁平程度会随着故障程度的增加越来越高。

航空发动机气路故障静电监测技术研究

航空发动机气路中任一部件的故障都会严重影响发动机的性能,甚至造成严重后果。本文研究了基于静电监测机理的发动机气路碎屑监测技术,设计了一套航空发动机气路故障静电监测系统,并在发动机模拟试验台上进行了两种典型故障模拟试验。研究表明,该系统通过监测发动机排气颗粒的静电电荷水平和变化规律,可判断发动机气路部件的故障情况,为发动机故障诊断和健康管理提供关键技术手段。

基于不确定观测下离散事件系统可诊断性的研究

在实际应用系统中,由于传感器故障、传感器限制和网络中的数据包丢失等原因,事件的可观测值变得不确定,使得观测系统行为变得尤为复杂。针对离散事件系统中,同个事件串可能有多个观测值以及不同状态下同个事件观测值也可能不同的问题,提出一种不确定观测下故障诊断验证的方法。首先对不确定观测的离散事件系统的可诊断性进行形式化,然后构建出用于上述故障诊断验证的验证器;基于验证器提出了系统基于不确定观测下可诊断的充要条件及验证算法;最后,实例说明不确定观测下故障诊断验证算法的应用。与现有研究相比,提出的方法对故障事件的观测值没有约束,可以为0个或多个观测值,使此方法应用的场景更为广泛。

空间飞行器自主诊断重构的理论和方法

面向我国深空探测任务对空间飞行器安全可靠自主运行提出的迫切需求,研究了空间飞行器自主诊断重构技术。针对空间飞行器诊断重构能力确定于设计之初、受制于有限资源等特点,提出了着力于系统评价设计过程的自主诊断重构方法。首先,分析了国内外研究现状并梳理了后续的待突破难点。其次,以可诊断性与可重构性理论为指导,提出了正常模式与故障模式一体化设计方法、故障诊断与系统重构一体化设计方法,在设计阶段通过资源配置和诊断重构方案的综合优化实现系统诊断重构能力最大化。然后,提出了基于能力在轨评估的诊断重构方案动态调整方法,在运行阶段通过诊断重构过程的自适应调整保持系统最优状态,进而从全寿命周期提升系统的诊断重构能力。最后,进行了总结与展望。

基于递归图和张量分解的故障可诊断性评价方法

可诊断性反映了系统故障诊断的难易程度,对系统进行可诊断性评价是开展故障诊断工作的前提条件。针对可诊断性评价问题,将测点信号考虑在内,提出了一种基于递归图和张量分解的故障可诊断性评价方法。利用相空间重构技术对装备在不同状态下的测点信号进行图形化表示,形成递归图;对递归图进行分析以提取其中的特征,并将该特征视为原信号特征;通过张量分解的方法,计算不同状态下信号特征之间的相似程度,作为故障诊断难易程度的基本度量。通过仿真实验对所提出的方法与仅考虑系统模型的D矩阵的可诊断性评价方法进行对比,结果表明该方法在评价可诊断性方面具有准确性和客观性。

基于多信号流图和相似性度量的故障可诊断性评价方法

针对目前基于定性模型无法对电子系统故障可诊断性进行定量评价的问题,将定性模型与数据驱动方法相结合,提出一种基于多信号流图和相似性度量的故障可诊断性评价方法。首先,根据系统的组成结构建立多信号流图模型并得到故障-测试相关性矩阵,基于故障-测试相关性矩阵提出了故障可诊断性评价准则。其次,提取测试信号的小波包Shannon熵作为特征向量,以欧式距离作为相似性度量指标,将故障可诊断性定量评价问题转换为不同故障模式下测试信号特征向量的相似性度量问题。然后,构建故障可诊断性评价矩阵,依据故障可诊断性评价矩阵提出了系统可诊断性指标。最后,通过仿真分析验证了所提方法的有效性。结果表明,所提方法在不构建数学模型的前提下,能够实现电子系统的故障可诊断性的定量评价。

基于SGMD-CMAE和WOA-ELM的滚动轴承故障诊断方法

针对滚动轴承的振动信号因其非平稳性和信噪比不高而难以准确提取特征的问题,提出了一种抗噪性能好、识别率高的滚动轴承故障诊断方法。首先,使用辛几何模态分解(symplectic geometry mode decomposition,SGMD)将滚动轴承故障信号分解为多个辛几何分量(symplectic geometry component,SGC),基于相关性原则,选取相关性高的SGC对故障信号进行重构,形成重构信号;然后,提出了复合多尺度注意熵(composite multi-scale attention entropy,CMAE)定量提取重构信号的特征熵值,构建CMAE特征;再选择鲸鱼优化算法(whale optimization algorithm,WOA)对极限学习机(extreme learning machine,ELM)中的有关参数优化处理,构建WOA-ELM模型;最后,将CMAE特征输入到WOA-ELM模型中,实现滚动轴承的故障诊断。仿真实验结果表明:与其他方法相比,文章所提的SGMD-CMAE和WOA-ELM方法识别滚动轴承故障准确率更高。