基于两种ANN模型的飞机发动机故障诊断分析

发动机故障的精确诊断关系到航空公司、维修单位的运营安全和经济效益。本文将反向传播算法和径向基函数在诊断某发动机故障的诊断效果进行分析比较,研究这两种方法在诊断发动机故障方面的优劣。     

诊断数控机床故障的方法

数控机床集多种技术于一体,诊断其故障难度较大。掌握诊断数控机床故障方法是确保数控机床发挥效能的关键,本文详细介绍了诊断数控机床故障的步骤和方法。     

基于大数据技术的电网故障智能诊断方法

针对目前已有的电网故障智能诊断技术在诊断方式上较多采用单一智能诊断方法,协同研究少,信息的综合利用少等问题,本文提出了基于大数据技术的电网故障智能诊断方法。首先简要介绍了调控大数据和智能诊断技术的现状及发展趋势。接着,分别对故障分类方法、故障预判方法、故障相关性分析、设备寿命分析评估四个方面所采用的算法和应用方式进行了研究。通过多种智能诊断技术的融合使用,极大的提高了电网故障诊断技术的智能化水平。     

生产线故障停机诊断过程的本体构建与实现

为提高生产制造设备的有效作业率,减少故障停机时间,本文提出一套指导生产线上故障停机诊断知识的本体构建原理和规则。以冲压车间为研究背景,基于对故障诊断过程的分析,建立以故障现象、故障原因、方案推荐和故障源4个基本知识域为核心的本体结构,并根据基本知识域间的语义关系,建立域间知识的多重关联,为故障停机诊断过程的语义推理、查询和智能理解奠定基础。最后,介绍以本体为内核的故障停机诊断系统,该系统主要实现故障停机诊断知识的语义标注和知识推理。     

汽油机油电路故障诊断与排除

汽车发动机故障的诊断,往往由于油、电路故障同时出现,故障现象又往往相似,混杂一起,故比油路和电路的单独故障诊断要复杂的多。例如可燃混合气过稀与点火过迟的故障,都具有使汽车行驶无力,急加速时化油器出现回火的现象。这就给综合故障的诊断与排除增加了不少难度。为此应在掌握油、电路单项故障的诊断作业基础上,进而着手综合故障的诊断。     

六诊诊断法在异步电动机故障诊断中的应用

根据异步电动机在实际生产中的故障发生情况,介绍了六诊诊断故障的诊断步骤和判断故障的思路和方法,阐述了如何对故障进行分区划块、缩小范围,提高故障诊断的准确度和诊断效率。在生产中能有效的排除异步电动机的故障。     

离合器常见故障的诊断与排除

传动系的故障是汽车发动机故障的常见故障,而离合器的故障在传动系统故障中占有很大比例,然而诊断离合器的故障是一项技术性很强的工作。虽然离合器的故障常常发生,但是故障往往不能得到很好的排除。因此,有必要对离合器的常见故障的诊断与排除方法作一介绍,就离合器的常见故障的诊断和排除进行详细的介绍,针对离合器常见的五种故障进行具体分析,并给出了排除故障的详细措施。     

智能化仪表的故障诊断与检修处理

智能仪表在使用过程中出现故障或损坏,需要尽快进行诊断和故障处理,使仪表能够恢复正常使用价值。本文主要阐述了智能化仪表一般故障的诊断方法和处理方法等技术问题。     

一种新颖的排水系统故障诊断方法

城市泵站排水系统是广地域分布的复杂系统,被控对象数量多、值守要求高。本文提出了一种基于网络的视频监控故障自主诊断查寻方法。给出了诊断控制系统的结构图,详细介绍了各环节的主要功能和工作原理。该诊断系统以排水系统的实时数据及关联数据图像信息为故障源的综合判断依据,并作为故障预处理信息。实际运行结果表明,该控制系统故障、诊断可信度高、性能稳定、安全实用。     

浅谈现代汽车故障自诊断技术

文章首先对故障自诊断的基本原理及组成作了概括,然后对故障自诊断工具——解码器以及故障自诊断技术运用作了介绍,再对经典车型的故障自诊断系统和故障分析时的注意事项作了阐述,最后对故障自诊断技术的新发展作了描述。     

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。     

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.     

Event-based weighted residual generator design via non-PDC scheme for fault diagnosis in T–S fuzzy systems

This paper is concerned with the event-based weighted residual generator design via non-parallel distribution compensation (PDC) scheme for fault diagnosis in discrete-time T–S fuzzy systems, under consideration of the imperfect premise matching membership functions. An event-triggered mechanism is firstly introduced to save communication resources, which leads to the premise variables of the system and observer to be asynchronous. Then, a fuzzy diagnostic observer with mismatched premise variables is designed to estimate the unmeasurable states of the system. Moreover, by using non-PDC method, a diagnostic observer-based weighted residual generator is established to improve the fault detection (FD) performance by using the information provided by each local system, in which the membership functions structure of the diagnostic observer and residual generator need not to be the same as the systems, and the L_∞/L₂ and L_∞ FD scheme is used to optimize the FD performance. Finally, two simulation results are provided to show the efficiency of the proposed non-PDC method.     

A practical propagation path identification scheme for quality-related faults based on nonlinear dynamic latent variable model and partitioned Bayesian network

In this paper, a practical technology or solution of quality-related fault diagnosis is provided for nonlinear and dynamic process. Unlike traditional data-based fault diagnosis methods, the alternative approach is focused more on identifying the propagation path that combines diagnostic information and process knowledge. The new method addresses the quality-related fault detection issue with developed nonlinear dynamic latent variable model for extracting nonlinear latent variables that exhibit dynamic correlations, then the advantage of relative reconstruction based contribution approach is followed to analyze the potential root-cause variables. Meanwhile, a new partitioned Bayesian network methodology is proposed for propagation path identification of quality-related faults. Finally, the whole proposed framework is applied to a real hot strip mill process, where the effectiveness is further demonstrated from real industrial data.     

网络故障的处理

首先介绍了网络和路由器的基本知识,在此基础上,进一步阐述了网络故障的诊断技术和网络故障的分层诊断技术,以及路由器接口故障的排除方法。     

红外诊断技术在电气设备管理中的应用

在电力系统普及、应用和推广红外诊断技术,是电力系统发展和电力安全、提高供电可靠性和降损节能的迫切要求。文章阐述了电气设备发热的原因和热故障种类,并详细提出了红外诊断技术在实际应用中的方法和注意事项。     

Data-driven fault diagnosis for an automobile suspension system by using a clustering based method

This paper concerns the issues of fault diagnosis and monitoring for an automobile suspension system where only accelerator sensors in the four corners of the car body are available. A clustering based method is proposed to detect the fault happened in the spring, and the Fisher discriminant analysis is applied to isolate the root factor for the fault. Different from most of the existing approaches, the pure data-driven characteristic enables this method to serve as an on-line fault diagnosis and monitoring tool without suspension model or fault features known as a prior. Moreover, this method can classify different reductions in the spring coefficient into one fault rather than different faults. The effectiveness of the proposed method is finally illustrated on an automobile suspension benchmark.     

基于Labview的伺服系统故障定位技术研究与实现

现代伺服系统规模庞大,结构复杂,且通常工作环境恶劣,故障发生率高,故障诊断费时费力。为了提高伺服系统故障诊断定位效率,基于故障树分析和虚拟仪器技术相结合在LabView软件平台上开发了伺服故障诊断定位系统。设计了一种基于最小割集表示的等价故障树的,结合模拟退火粒子群算法对神经网络的训练进行优化,使其故障诊断定位响应时间和准确度都显著提高。最后实验证明所设计系统采用的分析算法能够快速准确的进行故障诊断定位,对故障诊断定位技术的发展具有一定的参考和实际意义。     

Active fault diagnosis for a class of closed-loop systems via parameter estimation

This paper addresses an active fault diagnosis problem for a class of discrete-time closed-loop system with stochastic noise. By introducing the theories of system identification, a novel active fault diagnosis method is developed to detect and isolate the faults. An important advantage of the proposed method is that there is no need to cut off the original input signal, which is necessary in most active fault diagnosis methods. Firstly, due to the features of the faults, we transform the problem of fault diagnosis into a problem of model selection by estimating model parameters. Then, the sufficient condition for active fault diagnosability is analysed, and the property that auxiliary input signal can enhance the fault diagnosability is given. Finally, simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method.     

浅谈变压器穿越电流分析计算方法

降压变压器二次侧发生各种形式短路时,在一次侧会引起一定的穿越电流。当变压器连接方式不同,二次侧短路形式不同时,一次侧引起的穿越电流分布也不同。对称分量法是解决这一问题的重要方法。现行各种版本的供电技术教材对该部分内容介绍过于简单,一般直接给出结论,忽略了必要的分析和具体的推导过程。针对上述问题,简要介绍了对称分量法的基本概念,并举例说明了利用对称分量法求解变压器穿越电流值的具体过程。