A Study on FMS's Real-time Fault Diagnosis Expert System

ＡＳｔｕｄｙｏｎＦＭＳ＇ｓＲｅａｌ－ｔｉｍｅＦａｕｌｔＤｉａｇｎｏｓｉｓＥｘｐｅｒｔＳｙｓｔｅｍＸＩＡＯＸｉａｎｇｓｈｅｎｇ；ＬＩＵＷｅｎｊｉａｎ；ＳＵＢａｏｈｕａ；ＭＡＹｕｌｉｎ（肖祥胜，刘文剑，苏宝华，马玉林）（ＦＭＳＲｅｓｅａｒｃｈＣｅｎｔｅｒ...

STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

From the requirements of industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool ,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment, strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the real-time requirement of DC hoist,but also can give correct diagnosis results.

The Research of the Equation Model on the System-level Fault Diagnosis

In this paper we propose an equation model of system-level fault diagnoses, and construct corresponding theory and algorithms. People can turn any PMC model on ex-test into an equivalent equation (or a system of equations), and find all consistent fault patterns based on the equation model. We can also find all fault patterns, in which the fault node numbers are less than or equal to t without supposing t-diagnosable. It is not impossible for all graphic models.

On Fault Diagnosis of Rotating Machinery Using Wavelet Time-division Scale Level Moment

Based on an in-depth study of wavelet gray moment, we proposed a concept of a time-division scale level moment and gave the specific definition; ulteriorly, we discussed the factors which affected the fault diagnosis ability of a time-division scale level moment. The analysis results in the caculation of six typical fault signals show that the time-division scale level moment can be used to display the detailed information of a wavelet gray level image, extract the signal＇s characteristics effectively, and distinguish the vibration fault. Compared to the method of a wave gray moment vector, the method mentioned in this paper can provide higher calculation speed and higher capacity of fault identification, so it is more suitable for online fault diagnosis for rotating machinery.

Currentless Multiple Switch Open-Circuit Faults Diagnosis for Modular Multilevel Converters with Nearest Level Modulation in HVDC Systems

Due to the large number of submodules(SMs),and modular multilevel converters(MMCs)in high-voltage applications,they are usually regulated by the nearest level modulation(NLM).Moreover,the large number of SMs causes a challenge for the fault diagnosis strategy(FDS).This paper proposes a currentless FDS for MMC with NLM.In FDS,the voltage sensor is relocated to measure the output voltage of the SM.To acquire the capacitor voltage and avoid increasing extra sensors,a capacitor voltage calculation method is proposed.Based on the measurement of output voltages,the faults can be detected and the number of different-type switch open-circuit faults can be confirmed from the numerous SMs in an arm,which narrows the scope of fault localization.Then,the faulty SMs and faulty switches in these SMs are further located without arm current according to the sorting of capacitor voltages in the voltage balancing algorithm.The FDS is independent of the arm current,which can reduce the communication cost in the hierarchical control system of MMC.Furthermore,the proposed FDS not only simplifies the identification of switch open-circuit faults by confirming the scope of faults,but also detects and locates multiple different-type faults in an arm.The effectiveness of the proposed strategy is verified by the simulation results.

Design on Vibration Monitoring and Fault Diagnosis System of Large Water Pump Motor

Large water pump motor,whose operation decides the reliability of the whole production line,plays a very important role.Therefore,its online condition monitoring can help companies better know its operation,process fault analysis and protection.The essay mainly studies and designs large water pump motor′s real time vibration monitoring and fault diagnosis system.The essay completes the systems project design,the establishment of the system and performance test.Eddy-currentsensor,XM-120 vibration module,XM-320 axial translation module,XM-362 temperature module,XM-360 process amount module and XM-500 gateway module are used to measure the axial vibration and displacement of main motors.Laboratory tests prove that the system can meet the requirements of motor vibration monitoring.

A Novel Real-Time Fault Diagnostic System for Steam Turbine Generator Set by Using Strata Hierarchical Artificial Neural Network

The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.

A Novel Approach for Fault Diagnosis in Wireless Sensor Networks

The following article has been retracted due to special reason of the author. This paper published in Vol.5 No. 2, 2013, has been removed from this site.

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.

面向残差评价的中点钳位型逆变器开路故障诊断方法

在基于解析模型的故障诊断系统中,为了显化残差信号所包含故障信息的能力,提出一种面向残差评价的中点钳位型三电平三相逆变器快速开路故障诊断方法。首先,构建诊断观测器获取三相电流观测值并通过观测矩阵计算电流残差;其次,通过引入基于范数的标准评价函数,计算残差性能指标与阈值,将残差转换为可用的特征信号并与阈值相比较实现高效实时的故障检测;再次,推导不同功率开关器件开路故障状态下的电流残差特性,并依据此特性实现精准的故障隔离;最后,在仿真平台及半实物实验平台上验证了该文所提算法的有效性与可行性。仿真与实验平台结果表明,相较于传统基于信号与知识的诊断方法,残差性能评价的引入使得诊断速度提高80%以上。针对NPC型三电平三相逆变器常见的四种开路故障情况,均可实现快速且精准的故障检测及定位。

Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network

The faults in switched reluctance motors(SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface(GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

Primary Research on Real-Time Fault Diagnosis Platform for Fuel Tank System of an Aircraft

Sub-tanks in fuel tank systems of aircrafts transfer fuel to engines in certain order. These sub-tanks and attached tank-accessories affect each other, and make fault diagnosis in such systems rather difficult. Without real measured data, this paper analyzes fault modes and fault effects of the fuel tank system, including its tankaccessories, of a given aircraft. Fault model of the system is built theoretically, and fault diagnosis criteria are deduced. Such criteria are then quantified to train a back propagation neural network(BPNN) as fault diagnosis model. To realize fault diagnosis of the real fuel tank system, a real-time fault diagnosis platform based on Lab View and Vx Works to perform this diagnosis method is discussed. This platform is a technical groundwork for fault diagnosis in real fuel tank systems.

基于一维卷积子域适应对抗网络的变负荷轴承故障诊断

在大型旋转机械滚动轴承故障诊断的建模中,由于设备运行负载不同,若训练数据与测试数据具有分布差异,则会使训练得到的深度神经网络诊断模型的准确率下降。针对此问题,基于迁移学习理论,提出了基于一维卷积子域适应对抗网络的故障诊断方法。该方法嵌入了融合样本级权重的局部最大均值差异来促进子域对齐,并引入域判别器与特征提取器进行对抗训练,辅助提取域共性特征。建立了一种有效的跨负载轴承故障诊断模型,实现了目标域的无监督故障诊断,提高了滚动轴承故障诊断的准确性。最后,在凯斯西储大学发布的轴承故障数据集上进行实验,实验结果验证了所提方法的有效性。

适用于有源区域电磁波时程精细积分仿真的大时间步长矩阵数值积分方法

时程精细积分(PITD)法是一种全波电磁数值方法,具有相当宽松的数值稳定性条件,且数值色散误差几乎不受时间步长的影响,因而近年来在电磁学领域中受到广泛关注。在有源区域的电磁数值模拟中,PITD法需要计算激励源添加引入的非齐次项所产生的矩阵积分,其计算或存在矩阵求逆运算,易不稳定,或难以在大时间步长下保证计算结果的精度,这使得PITD法的推广应用受到一定限制。该文将响应矩阵法引入有源区域的电磁波PITD仿真中,不仅避免了矩阵的求逆运算,并且能够在保证计算结果精度的条件下,增大时间步长的选择范围,提高算法的计算效率。最后,进行了实例计算,并与直接近似积分方法和数值积分方法的结果进行了对比,验证了该文所提方法的实用性和有效性。

核电厂安全级DCS缺省值设置策略研究

针对数字化仪控系统中无效信号的质量位随意蔓延使系统处于一种不确定状态的问题,结合核电厂运行工况和信号特性,对龙鳞平台故障诊断机制和信号质量位标识进行研究。考虑故障安全准则,系统性地提出缺省值设置原则。从信号执行功能和信号边界两个维度进行分析,确认缺省值的设置范围,并详细给出执行保护功能、报警功能、维护和试验功能信号的缺省值设置策略。同时,针对传统的缺省值验证方式无法全面、有效地进行缺省值验证的问题,提出一种利用全范围模拟机和虚拟数字化控制系统(DCS)进行缺省值验证的新方法。利用该方法可有效地对DCS内设置的缺省值进行系统性的验证。所提出的缺省值设置策略和验证方法可为后续核电厂安全级DCS的缺省值分析和设置提供全面的指导。

增强型罗兰发射机典型比较电路分析与仿真

比较电路在增强型罗兰固态发射机电路中使用广泛,尤其在幅度、定时控制以及故障检测中,比较器电路往往起到重要作用,是固态发射机输出稳定、精准的导航授时信息的关键。掌握这些典型电路对于提高发射机运维能力很有帮助,文章针对不同比较电路的典型应用场景展开分析,并利用LTspice软件对分析结果进行了仿真验证,验证结果显示该方法对于开展板级电路故障诊断和维修有一定的指导意义。

T型三电平逆变器开路故障诊断方法研究

本文以三相T型三电平逆变器为研究载体,以开关器件开路故障作为研究对象,采用“数据采集-特征提取-特征识别-软硬件部署”的工作范式,提出基于数据驱动的开关器件故障诊断方法。通过搭建完整诊断仿真平台,在基波周期时间尺度给出诊断结果,取得了95%以上的故障诊断准确率。

一种基于MCU诊断的STO功能电路在电梯控制系统中的应用

传统的电梯控制系统具有控制柜体积大、布线复杂、接触器易磨损等缺点。本文依据GB/T 12668.502-2013国家标准要求,设计了一种具有故障诊断功能的双通道架构的STO功能电路,并将其应用于电梯控制系统。实验证明:该功能电路安全完整性等级为SIL3,硬件故障裕度为1,满足GB/T 7588.1-2020国家标准对电梯控制系统的要求。

双闭环比值控制系统传感器故障诊断方法

双闭环比值控制系统由于主从回路滞后效应和闭环回路调节作用,使其故障诊断较为复杂。针对该系统传感器故障难以识别的问题,通过分析反映系统动态特性的实时数据,提出一种基于数据驱动的故障诊断方法。利用拉伊达准则标定检测阈值建立故障检测模型;利用传感器故障传播的滞后特性建立故障定位模型;利用突变故障的幅值特性建立了故障估计模型;利用故障调节动态过程的变化趋势建立了故障分离模型;通过在线数据标定获得了故障诊断动态模型。通过实验验证该方法的有效性和高精度。研究结果表明,所提方法适用于一般双闭环比值控制系统传感器的实时故障诊断,其中故障检测准确率可达98.5%,故障定位准确率可达98.5%,故障估计准确率可达99.28%,故障分离准确率可达98%。

基于双通道数据主振动特征提取的水电机组轴系故障诊断

相对于特征层和决策层融合,数据层融合可以充分保留数据中的原始诊断信息,从而为后续故障诊断奠定基础。为此,提出一种基于双通道数据主振动特征提取的水电机组轴系故障诊断方法。该方法首先利用主成分分析(Principal Component Analysis,PCA)对轴系双通道时域振动信号进行数据层融合,得到最大振动强度方向上的合成振动(简称主振动);随后,对主振动进行特征提取,再利用支持向量机(Support Vector Machine,SVM)进行分类,从而实现故障诊断。利用转子台数据对所提方法进行验证。结果表明,与直接利用单通道进行诊断或通过双通道进行特征层融合诊断相比,所提方法可以消除主振动方向变化带来的影响,提升类内聚类和类间分离的效果,最终提高故障诊断的准确率。