基于非线性卡尔曼滤波器的双通道传感器故障检测及隔离方法

航空发动机是典型的非线性系统,传统的基于线性模型的故障诊断算法忽略了高阶非线性特性,对诊断结果有一定的影响.本文建立了发动机改进平衡流形展开模型,提出了基于改进的平衡流形展开模型与容积卡尔曼滤波器相结合的双通道传感器故障诊断及隔离方法,利用双传感器的冗余性实现了快速灵敏地故障检测与隔离.所提方法在CFM-56发动机的模拟机上进行仿真试验,试验结果证明了所提方法的有效性.     

基于混合算法的燃气轮机多传感器故障诊断

为解决卡尔曼滤波算法难以实现燃气轮机多传感器故障诊断的难题,提出一种基于混合算法的燃气轮机多传感器故障诊断方法。首先,基于平方根容积卡尔曼滤波（SRCKF）算法构建了一组滤波器,每个滤波器对状态的最优估计被定义为故障检测因子用于传感器故障的特征提取;然后,利用基于密度的聚类算法对故障检测因子进行聚类以实现故障传感器的检测和隔离;最后,利用极大似然估计方法（MLE）实现故障传感器故障严重程度的估计。所提出的方法在GT25000三轴燃气轮机模拟机上进行了仿真验证,仿真结果表明：所提方法有效,多传感器故障诊断的准确率高于95%。     

基于多模型估计的转子裂纹故障诊断方法

转子裂纹故障的早期诊断与预示是当前转子动力学领域的一个难点。针对这个问题,提出了基于多模型估计(MMAE)的转子裂纹故障诊断方法。针对Jeffcott转子建立了正常、裂纹转子模型和基于卡尔曼滤波器的多模型自适应估计器,通过裂纹故障的仿真分析和故障多模型估计表明,该方法对早期诊断和预示转子裂纹故障有良好的效果。     

一种面向测量偏差的传感器故障诊断方法

传感器是任何测控系统中必不可少的部件,而传感器在高温、振动、腐蚀性等恶劣环境下工作,极易发生故障,因此,开展传感器故障诊断意义重大。常规的传感器故障诊断方法是直接分析传感器的测量值,这种方法测得传感器的故障信号不灵敏,且无法实现变工况下的诊断。因此,基于性能仿真模型与D-S证据理论信息融合技术,提出一种面向测量偏差的传感器故障诊断方法,并应用于燃气轮机传感器故障诊断。仿真试验结果标明,该方法可以提高监测传感器故障的灵敏度,准确地识别出各类传感器故障,并进行数据恢复,减少不必要的停机。     

基于GAPSO-FNN神经网络的锂离子电池传感器故障诊断北大核心CSCD

新能源汽车的动力电池内部存在多种传感器用来进行电池系统的安全监测,而传感器故障会导致荷电状态等指标出现误差,严重时会触发电池热失控的风险。为了有效准确地进行电池传感器故障诊断,提出基于遗传算法优化粒子群算法(genetic algorithm optimized particle swarm optimization,GAPSO)和模糊神经网络(fuzzy neural network,FNN)的锂离子电池传感器故障诊断方法对锂离子电池的传感器进行故障诊断,该方法使识别故障准确率迅速提升。本工作首先通过硬件平台和Matlab/Simulink环境相结合的方式获取电池传感器故障的数据,然后对故障数据进行预处理及特征提取,最后采用GAPSO-FNN算法对电池传感器进行故障诊断,并与传统神经网络和模糊神经网络方法的结果进行对比。仿真结果表明,基于GAPSO-FNN的锂离子电池传感器故障诊断方法相比于传统的神经网络方法测量准确率提升了25%,相比于模糊神经网络准确率提升了10%,故障诊断准确率能够达到95%,在减少故障诊断所需信息量的同时,有效地提升了故障诊断的准确率。     

PMSM的电流传感器故障诊断与容错控制

以永磁同步电机(PMSM)的矢量控制系统为研究对象,在空间矢量脉宽调制(SVPWM)算法的基础上,提出一种电流传感器的故障诊断及其容错控制方法。首先分解直流电流来粗略重构定子电流,通过对重构的定子电流与测量的定子电流进行比较,来判断各个电流传感器是否发生故障。若定子侧故障,修正重构电流并取代该故障电流;若直流侧故障,用定子电流重构直流电流。利用Matlab/Simulink进行仿真,仿真结果表明该方法能够快速准确的判断出直流侧或定子侧的电流传感器的故障并实现容错控制。     

基于小波神经网络方法的空调系统传感器漂移故障诊断

对空调系统中的温度、压力、流量传感器的漂移故障,提出了一种基于小波神经网络的传感器故障诊断方法.该方法首先采用小波分析方法对历史故障数据和正常数据进行分析,从而提取数据的频带特征,通过神经网络对这些特征进行学习,使神经网络分析能够对待诊断数据的进行故障诊断.仿真实验的结果表明,该方法对传感器的漂移故障能够实现有效地诊断.     

氧传感器在诊断电喷发动机故障中的应用

电喷发动机中的氧传感器是电子控制燃油喷射系统中重要的传感器之一.氧传感器可以对发动机气缸内部的燃烧起监控和指示作用.笔者在电喷发动机故障诊断试验台上,通过测取发动机相关参数及氧传感器波形等信号,验证氧传感器在诊断电喷发动机故障中的作用.     

基于NLMS-WP及BP的风机轴承故障诊断方法

为解决风机轴承故障诊断问题,全面提取轴承运行状态的特征信息,提出了基于NLMS与WP相融合的特征提取及神经网络相适配的故障诊断方法。首先采用自适应滤波器对故障信号进行滤波去噪,再利用小波包对信号进行分解重构并提取其能量特征,将小波包各个频段的能量比系数作为风机轴承的故障特征,并通过改进的神经网络模型分类识别轴承的故障信号,实现不同类型的轴承故障诊断。试验结果表明,该方法弥补了传统故障诊断方法的不足,提高了故障类型识别率和故障诊断准确率,诊断效果良好。     

用卡尔曼滤波器对河流的水位和流量进行预报

本文中把曼宁阻力系数n当作卡尔曼滤波器的状态变量,把水位h当作为观测量,所得的这种形式的卡尔曼滤波器与Simons的形式进行比较,其计算结果表明本篇形式的滤波器误差较小。     

Faults diagnosis for PEM fuel cell system based on multi-sensor signals and principle component analysis method

Fuel cell vehicles are becoming more popular and attracting more attention from industries, but stability and reliability of the fuel cell system (FCS) are still problems for its commercial progress. Therefore, a fault diagnosis system is essential for a reliable and long working lifetime FCS. In this work, a fault diagnosis method based on multi-sensor signals and principle component analysis (PCA) is proposed to improve FCS performance. By using this method, the correlation among different sensor signals are analyzed based on multi-sensor signals, and a simplified statistic index for fault diagnosis is deduced based on the PCA. The FCS operation conditions are monitored online, and faults in sensor and system levels are diagnosed. Experimental results show that, two typical fault scenarios, i.e., a single sensor fault and a serious system failure, can be successfully diagnosed and distinguished. For the single sensor fault, the sensor signal is reconstructed immediately to ensure that fuel cell vehicles operate normally. For the system failure, the fault can be detected in 17 s and the fault source signals can be located in 31 s, so the fuel cell stack can be protected timely. The main contribution of this work is to deduce a simplified statistic index for fault diagnosis based on multi-sensor signals and PCA method, and to provide an experimental study on identifying faults in sensor and system levels of a PEM fuel cell system.     

基于SOM神经网络的柴油机燃油系统故障诊断

利用神经网络的非线性映射,及其高度的自组织和自学习能力,将SOM网络应用于柴油机的故障诊断。利用夹持式传感器获得柴油机喷射系统的燃油压力波形,对波形进行时域分析和特征提取。根据所取得故障信息及其对应的故障类型来构造网络结构,用单一故障样本对网络进行训练,根据输出神经元在输出层的位置对故障进行判断。通过仿真实验验证SOM神经网络在柴油机故障诊断的正确性。经实例分析证明,该方法可对故障进行有效诊断。     

基于信息融合技术的发动机故障诊断的研究

研究了信息融合技术在电控发动机故障诊断中的应用。研究结果表明,基于神经网络的特征层信息融合诊断效果明显优于单一传感器,而且可实现信息压缩,进行实时处理与诊断;基于Dempster-Shafer证据推理的决策层信息融合,可对异质传感器信息进行非同步处理,对发动机故障分类准确性高、可靠性强,但融合精度不及特征层融合方法,预处理代价高。在实际应用中,应根据传感器类型、信号预处理方式、系统的复杂程度等合理选择信息融合方法。     

Sensor fault detection and validation of VAV terminals in air conditioning systems

Sensor failure and bias are harmful to the process control of air conditioning systems, resulting in poor control of the indoor environment and waste of energy. A strategy is developed for the flow sensor fault detection and validation of variable air volume (VAV) terminals in air conditioning systems. Principal component analysis (PCA) models at both system and terminal levels are built and employed in the strategy. Sensor faults are detected using both the T² statistic and square prediction error (SPE) and isolated using the SPE contribution plot. As the reliability and sensitivity of fault isolation may be affected by multiple faults at the system level, a terminal level PCA model is designed to further examine the suspicious terminals. The faulty sensor is reconstructed after it is isolated by the strategy, and the FDD strategy repeats using the recovered measurements until no further fault can be detected. Thus, the sensitivity and robustness of the FDD strategy is enhanced significantly. The sensor fault detection and validation strategy, as well as the sensor reconstruction strategy for fault tolerant control, are evaluated by simulation and field tests.     

An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems

Principal component analysis (PCA) has been found to be powerful in detecting sensor faults in multivariate processes, but it is inefficient in isolating faults due to its pure data-driven nature, especially when dealing with processes with strongly coupled multiple variables, such as the air-handling processes in typical variable air volume air conditioning systems. This paper presents an expert-based multivariate decoupling method to enhance the capability of the PCA-based method in fault diagnosis by taking advantage of expert knowledge about the process concerned. The decoupling method develops unique fault patterns of typical sensor faults by analyzing the physical cause-effect relations among variables. Through comparing fault symptoms reflected by the residual vectors of the PCA models with fault patterns, a sensor fault can be successfully isolated. The isolation enhanced PCA method is implemented and validated in a typical air-handling process. The test results show that the joint approach to enhance the fault isolation ability of the PCA-based fault detection and diagnosis method is effective. The robustness of the PCA-based sensor FDD method against component faults is also proved to be improved because the fault symptoms of sensor faults are unique.     

黑河流域梯级水电站群机组远程监控与故障诊断系统

为了确保水轮发电机组的安全稳定和经济运行,提出了基于Internet/Intranet开放式结构的水轮发电机组远程监测与故障诊断系统,并对系统的设计原则、网络化体系结构、机组测点布置、传感器选型及系统的功能做了详细的分析。该系统实现了水轮发电机组信号的数据采集、数据分析和处理,并能通过Internet/Intranet进行故障诊断。实际应用表明,该系统兼具技术的先进性和实用性,运行稳定可靠,具有良好的应用前景。     

机油含水率传感器及其在柴油机保护仪上的应用

本文介绍一种电化学阻抗谱（Electrochemical impedance spectroscopy,EIS）在线监测机油含水率传感器。通过对其大量试验结果的分析,论述用于在线监测机油含水率变化,以便对柴油机的安全运用起保护作用的可行性。     

Fuzzy observer-based fault tolerant control against sensor faults for proton exchange membrane fuel cells

In this paper, robust fault diagnosis problem of Proton Exchange Membrane Fuel Cells (PEMFC) is presented based on Takagi-Sugeno (TS) Fuzzy Unknown Input Observer (FUIO). TS FUIO based on Linear Matrix Equalities (LMEs) and the Linear Matrix Inequalities (LMIs) are design. Firstly, the nonlinear PEMFC system with sensor faults and disturbance is represented by TS fuzzy model. Then, a FUIO and sensor fault estimation algorithm is developed and then a model based Fuzzy Fault Tolerant Controller design uses the concept of Parallel Distributed Compensation (PDC). Sufficient stability conditions are studied based on LMIs and LMEs. In order to verify the proposed approach, a PEMFC system with return manifold pressure and hydrogen mass sensors fault and disturbance was tested to illustrate the effectiveness of the proposed strategy.     

锅炉系统故障树的建立与分析

根据锅炉系统的结构和功能的层次分解模型，对常见故障逐层进行分析诊断，采用故障树分析法，来描述系统故障和故障原因的逻辑关系，为锅炉系统的故障诊断、专家系统应用、面向对象技术的广义故障树知识表达和故障诊断推理控制奠定了基础。