工业锅炉故障诊断的改进数据挖掘方法

针对工业锅炉的常见故障,提出了一种基于数据挖掘方法的锅炉故障诊断技术.通过建立一个智能化的数据挖掘工具,直接从大量实时数据中获取故障诊断知识进行故障诊断.数据挖掘工具的核心是采用信息熵技术来辅助遗传算法初始种群的生成.遗传算法和信息熵的有效集成,极大地提高了该数据挖掘方法的工作绩效.将本方法应用于火电厂锅炉的一个复杂故障事例,结果表明其诊断的精度可以满足现场应用的要求.     

基于模糊诊断理论的故障诊断工具研究与实现

为实现电子信息装备的故障诊断需求,设计并实现了一种基于模糊诊断理论的故障诊断工具;该故障诊断工具以模糊诊断理论为基础,采用基于模糊关系矩阵的诊断方式,结合相关辅助诊断方法,采用Labwindows/CVI进行开发,能够准确地定位故障部位;测试结果采用数据库开发模式,以Access数据库对诊断结果进行存储;最后,以某型导弹装备为例,利用该诊断工具进行故障诊断,准确定位故障部位,验证了该工具的准确性、实用性和有效性.     

数据挖掘技术在机械设备故障诊断中的应用

介绍了一种基于数据挖掘技术的机械设备故障诊断方法,介绍了数据挖掘技术在故障诊断中的实现步骤.以数据挖掘系统在煤矿风机监控系统中的应用为例,建立了数据挖掘模型,详细论述了关联规则方法的挖掘过程,对挖掘结果进行了分析,表明此方法可以很好的对设备故障进行预警.     

基于极化因子神经网络的火电厂制粉系统故障诊断技术

制粉系统是火电厂的主要设备,其安全稳定运行对发电企业的经济生产具有十分重要的意义;针对制粉系统的运行特性和故障分析,提出了基于极化因子神经网络的火电厂制粉系统故障诊断方法,该方法将故障征兆相应的过程变量作为输入,将制粉系统故障类型作为输出,通过训练神经网络建立其系统故障诊断模型,其中训练过程中采用极化因子来自动调整神经网络的收敛速度,从而在满足误差目标的前提下,防止其陷入局部极小;选取实际火电厂制粉系统3个典型故障及其相对应的9个故障征兆参数进行了实验;结果表明,该方法具有良好的收敛性,完全可以满足火电厂制粉系统现场故障诊断的要求.     

生产过程故障诊断专家系统及其应用

吸取神经嘲络和专家系统两种方法的优点、弥补各自的不足,把启发式规则推理和神经网络诊断方法进行有机集成,建立一个集成专家系统,更加准确地模拟人脑的真实思维过程.以火电厂化学水处理为例,在采集了充分的故障数据后,结合典型的故障经验数据,进行了故障诊断的仿真试验,并给出实际应用效果,以此说明了该故障诊断专家系统的实用性.     

基于Apriori关联规则的汽轮机振动监测与故障诊断

针对火电厂汽轮机发生振动故障时仅依靠振动参数进行故障诊断而产生诊断时间长与诊断的原因与部位不具体的问题,提出了基于关联规则的汽轮机振动监测与故障诊断方法;分析了汽轮机振动故障产生时涉及到的振动参数与热力参数,研究了将热力参数与振动参数关联结合的故障诊断规则,确立了状态运行监测与故障诊断的系统设计思路;通过实际验证证明这种方法具有很强的实用型与可行性,对火电厂汽轮机振动故障设备的研发与改进有借鉴意义。     

数据挖掘技术在网络故障诊断中的应用

网络日常监控信息规模日益增大,人工数据分析方式将无法满足现代化发展要求,所以数据挖掘是处理该问题的重要方式.数据挖掘,作为一种全新的获取技术,通过在数据自身上挖掘出有价值的信息、故障智能诊断与分析、便于维修工作人员及故障诊断人员及时判断故障问题,提出了可行性处理意见并及时维修,便于网络恢复正常,提高网络运行效率与质量.以数据挖掘技术为重点,深入探讨了数据挖掘技术在网络故障诊断中的应用.     

一种基于粗集理论的车地无线通信设备故障诊断方法*

针对车地无线通信设备故障诊断信息不一致的情况下进行故障的推理和诊断,提出了一种基于粗集理论的车地无线通信装备故障诊断方法。该方法从诊断决策支持系统定义出发,将车地无线通信设备故障诊断问题用一个不同简化层次的故障决策网络表示,由网络节点根据定义出的规则置信度和覆盖度可推导出对应有效的故障诊断规则集合。通过故障诊断规则匹配,便可方便得出车地通信单元故障诊断结果,实例分析表明该方法具有较好诊断有效性。     

基于多小波熵灰色理论的故障诊断应用研究

为了有效地对其进行故障诊断,提出了一种基于多小波熵特征提取与灰色理论相结合的故障分类方法,小波熵测度由于结合了小波变换和信息熵理论的优势,能快速准确地提取电流信号故障特征,但由于设备故障的不确定性和多样性,依靠单一的小波熵测度诊断故障可能出现诊断困难或诊断失真等问题,对多种小波熵进行了特征提取,并结合灰色理论进行故障关联,以飞机液压试验台上采集的压力信号进行故障分类,试验结果表明该方法能提高对故障诊断结果的支持度及故障诊断的准确性和实时性,为设备故障诊断提供了一种可行的新方法。     

基于仿真与数据挖掘的故障诊断方法研究

针对导弹武器系统故障信息的特点,提出了系统仿真与数据挖掘相结合的综合故障诊断方法.文中将导弹武器系统故障检测信息分为3类,即:离散交互特征信息、连续动态特征信息和离散事件特征信息.通过实例阐述了数据挖掘在导弹武器系统故障诊断中的应用.分析了基于仿真的故障诊断方法,在此基础上,结合仿真与数据挖掘各自在故障诊断方面的优势,进一步提出了基于仿真与数据挖掘的综合诊断方法,给出了方法的步骤和诊断流程.     

基于同步压缩-交叉小波变换算法的齿轮故障诊断研究

齿轮超负荷使用或者在不良环境下使用时,容易造成齿轮失效。齿轮的失效形式主要有断齿故障、齿面点蚀、齿轮磨损等,此外齿轮制造过程中也存在固有误差,传统的齿轮故障诊断通常使用振动加速度传感器或者SCADA数据进行处理,但振动加速度传感器与SCADA通常价格昂贵,且会有大量的数据冗余,不便于后期信号处理。本设计拟采用一种同步压缩-交叉小波变换算法,在齿轮故障机理分析的基础上,设计了故障诊断实验装置,对正常齿轮、断齿和磨损情况下的故障特性进行提取,从而对故障进行准确诊断,经验证,该方法诊断准确度高。     

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 normal behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months.The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components.In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.     

Machine learning using version spaces for a power distribution network fault diagnostician

There has been much interest in the application of expert systems to a wide variety of power system problems. One important application is the diagnosis of electrical faults in power distribution systems. A common problem with expert systems is the ‘knowledge acquisition bottleneck’ which arises with the generation of rules for the expert system, and there is benefit in automating this procedure as much as possible. This paper presents a fault diagnostician which uses a version space to learn from data in a SCADA system. The end user specifies background knowledge for use by the version space algorithm, but other than this the procedure is automatic. A test system was implemented and evaluated with the aid of a distribution network simulator. The results of this evaluation are presented.     

数据挖掘技术在高速铁路SCADA中的应用研究

分析了高速铁路牵引供电SCADA系统的特点,及当前SCADA系统的不足,提出了用数据挖掘技术分析和预测变电所设备故障,改善系统的综合性能.以高速铁路牵引供电SCADA系统现有功能为基础,给出了挖掘过程的总体设计,并对其中的关键步骤,如SCADA挖掘库的建立和更新、挖掘算法的选取与改进、模型的训练与评估作了阐述.     

基于BP神经网络模型的故障预测分析

本文以数据采集与监控(SCADA)系统提供数据和设备故障的历史数据作为故障预测的数据来源,对动态数据和静态数据采用BP神经网络建模,测试结果说明了模型的有效性。     

基于大数据分析的风机叶片结冰故障诊断

为了实现风力发电机叶片结冰故障诊断,及时进行风机叶片除冰,消除隐患。提出了基于大数据分析的人工智能算法识别风机叶片结冰的方法。首先,用结冰机理研究和数据探索的方法对风机运行数据进行分析,初步提取了24个特征量;然后,采用遗传算法对24个特征量、滑动窗口宽度和支持向量机参数进行联合优化,并据此建立叶片结冰故障诊断模型。诊断结果表明,用该模型诊断叶片结冰故障的准确率为86.2%,比采用SCADA采集所有数据或初步提取的24个特征量作为模型输入的准确率有大幅度的提高;并且,将该模型用于另一个#2风机时,故障诊断准确率也达到了78.5%,证明了该方法的有效性,并具有较好的泛化能力,为识别风机叶片结冰故障提供了新思路。     

BA-PNN-based methods for power transformer fault diagnosis

This paper presents a machine learning-based approach to power transformer fault diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the probabilistic neural network (PNN). PNN is a radial basis function feedforward neural network based on Bayesian decision theory, which has a strong fault tolerance and significant advantages in pattern classification. However, one challenge still remains: the performance of PNN is greatly affected by its hidden layer element smooth factor which impacts the classification performance. The proposed approach addresses this challenge by deploying the BA algorithm, a kind of bio-inspired algorithm to optimize PNN. Using the real data collected from a transformer system, we conducted the experiments for validating the performance of the developed method. The experimental results demonstrated that BA is an effective algorithm for optimizing PNN smooth factor and BA-PNN can improve the fault diagnosis performance; in turn, and the machine learning-based model (BA-PNN) can significantly enhance the accuracies of power transformer fault diagnosis.     

基于数据挖掘及可信度理论的火电厂水汽化学过程故障诊断方法

迅速确诊火电厂水汽化学过程的故障是维持水汽品质高合格率、保证机组正常高效运行的有效手段,具有重要意义。鉴于当前上述诊断方法缺乏,本文设计了1种基于可信度理论的诊断模型,构建了合理的模糊诊断规则,将可信度推理处理不确定关系的优点应用于水汽化学过程故障诊断之中;利用数据挖掘技术确定征兆权值及规则阈限,解决了可信度推理受主观因素影响过大的问题;还改进了规则可信度的设定方法,使之更接近于专家思维。实例证明,该诊断模型具有较高的准确性和实用性。     

Variable-weighted Fisher discriminant analysis for process fault diagnosis

Variable-weighted Fisher discriminant analysis (VW-FDA) is proposed to improve the fault diagnosis performance of the conventional FDA. VW-FDA incorporates the variable weighting into FDA. The variable weighting is used to find out each weight vector for all faults. After all fault data are weighted by the corresponding weight vectors, the summed fault data can be constructed to magnify each fault’s local characteristics. Then, VW-FDA is performed on the summed fault data rather than the original fault data. It is helpful to extract discriminative features from overlapping fault data. Moreover, the partial F-values with the cumulative percent variation are used for exactly variable weighting, which is indispensable to VW-FDA. The proposed approach is applied to Tennessee Eastman process. The results demonstrate that VW-FDA shows better fault diagnosis performance than the conventional FDA.     

基于SCADA参量关联互信息自编码的风电机组故障检测方法

针对风力发电机组数据采集与监视控制系统(supervisory control and data acquisition,SCADA)监测参量间的耦合关联性,提出基于多参数耦合关联互信息编码的风电机组故障检测方法.该方法构建了SCADA数据的耦合关联矩阵,采用互信息变分自编码器对关联矩阵进行编码重构;将SCADA参量关联矩阵的重构误差作为机组健康评估指标,结合指数加权移动平均模型的迭代更新,对机组实时故障阈值进行自适应设置.两个风场的风电机组SCADA数据分析结果表明,所提方法充分利用了SCADA数据的耦合关联结构信息,能有效提高风电机组故障检测的准确性及对环境工况的鲁棒性.