Practical integrated navigation fault detection algorithm based on sequential hypothesis testing

In detecting system fault algorithms,the false alarm rate and undectect rate generated by residual Chi-square test can affect the stability of filters.The paper proposes a fault detection algorithm based on sequential residual Chi-square test and applies to fault detection of an integrated navigation system.The simulation result shows that the algorithm can accurately detect the fault information of global positioning system（GPS）,eliminate the influence of false alarm and missed detection on filter,and enhance fault tolerance of integrated navigation systems.

Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.

Robust fault detection filter and its application in MEMS-based INS/GPS

Since any disturbance and fault may lead to significant performance degradation in practical dynamical systems,it is essential for a system to be robust to disturbances but sensitive to faults.For this purpose,this paper proposes a robust fault-detection filter for linear discrete time-varying systems.The algorithm uses H∞ estimator to minimize the worst possible amplification from disturbances to estimate errors,and H_ index to maximize the minimum effect of faults on the residual output of the filter.This approach is applied to the MEMS-based INS/GPS.And simulation results show that the new algorithm can reduce the effect of unknown disturbances and has a high sensitivity to faults.

Real-time fault detection method based on belief rule base for aircraft navigation system

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base （BRB）, where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization （EM） algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.

Inner Attitude Integration Algorithm Based on Fault Detection for Strapdown Inertial Attitude and Heading Reference System

This article proposes a new inner attitude integration algorithm to improve attitude accuracy of the strapdown inertial attitude and heading reference system （SIAHRS） , which, by means of a Kalman filter, integrates the calculated attitude from the accelerometers in inertial measuring unit （IMU） , called damping attitudes, with those from the conventional IMU. As vehicle＇ s acceleration could produce damping attitude errors, the horizontal outputs from accelerometers are firstly used to judge the vehicle＇ s motion so as to determine whether the damping attitudes could be reasonably applied. This article also analyzes the limitation of this approach. Furthermore, it suggests a residual chi-square test to judge the validity of damping attitude measurement in real time, and accordingly puts forward proper information fusion strategy. Finally,the effectiveness of the proposed algorithm is proved through the experiments on a real system in dynamic and static states.

Receiver Autonomous Integrity Monitoring Availability and Fault Detection Capability Comparison Between BeiDou and GPS

This paper used the statistical methods of quality control to assess receiver autonomous integrity monitoring(RAIM) availability and fault detection(FD) capability of BeiDou14(Phase II with 14 satellites),BeiDou(Phase III with 35 satellites) and GPS(with 31 satellites) for the first time. The three constellations are simulated and their RAIM performances are quantified by the global, Asia-Pacific region and temporal variations respectively. RAIM availability must be determined before RAIM detection. It is proposed that RAIM availability performances from satellites and constellation geometry configuration are evaluated by the number of visible satellites(NVS, NVS > 5) and geometric dilution of precision(GDOP, GDOP < 6) together. The minimal detectable bias(MDB) and minimal detectable effect(MDE) are considered as a measure of the minimum FD capability of RAIM in the measurement level and navigation position level respectively. The analyses of simulation results testify that the average global RAIM performances for BeiDou are better than that for GPS except global RAIM holes proportion. Moreover, the Asia-Pacific RAIM performances for BeiDou are much better than that for GPS in all indexes. RAIM availability from constellation geometry configuration and RAIM minimum FD capability for BeiDou14 are better than that for GPS in Asia-Pacific region in all cases, but the BeiDou14 RAIM availability from satellites are worse than GPS's. The methods and conclusions can be used for RAIM prediction and real-time assessment of all kinds of Global Navigation Satellite Systems(GNSS) constellation.

亚轨道飞行器的惯性/天文/卫星多源稳健融合导航方法

为保证亚轨道飞行器导航系统在复杂环境下的精度和鲁棒性,针对卫星导航观测量异常粗差和大气密度误差引起的混合故障问题,提出了一种惯性/天文/卫星多源稳健融合导航方法。在惯性/卫星子系统利用双因子等价权调整滤波增益矩阵,能够削弱卫星导航相关观测时故障信息对状态估计的不良影响;在惯性/天文子系统利用惯性导航定位信息,设计了一种惯导辅助的星光折射故障诊断算法,可以有效检测和隔离大气密度误差引起的星光折射导航故障。对含有上述故障信息的场景进行了仿真,结果表明,该算法能够有效处理卫星导航观测异常和星光折射导航定位故障对导航结果的影响,具有较强的容错性能。在故障条件下,定位精度可控制在70 m左右,定速精度小于0.2 m/s,定姿精度在10″以内。

一种LM-BP加速搜索的周跳探测与修复方法

针对传统三频周跳探测与修复方法中的不敏感、漏检以及效率较低等问题,提出一种基于莱文伯格-马夸特(LM)-反向传播(BP)神经网络加速搜索法的伪距相位组合与电离层残差组合联合周跳探测与修复方法:利用2个伪距相位组合以减少不敏感周跳数量,利用1个电离层残差组合以提高小周跳探测敏感度;在构成3个线性无关的组合观测值后,使用LM-BP加速搜索算法进行周跳探测与修复。实验结果表明,相对常规的伪距相位组合与电离层残差组合联合方法,该方法能够提高周跳探测与修复性能,可探测小至1个的周跳,探测与修复整体时效有较大提升。

基于单类支持向量机的组合导航容错算法

提出了一种基于单类支持向量机(OCSVM)的组合导航容错算法。针对组合导航系统中子系统出现故障会影响整个导航系统精度的问题,采用基于单类支持向量机的方法,对故障进行检测和隔离,并对容错性能进行分析。仿真结果表明:在应用基于单类支持向量机的容错算法后,系统的故障检测模块可以有效地隔离故障数据,降低了多源组合导航系统的位置误差,其可靠性和稳定性也得到了提高。

考虑多扰动因子的含光伏电源低压台区漏电故障检测

针对含光伏电源的低压配电台区剩余电流检测易受多重因素干扰,难以实现漏电故障精准检测的问题,提出了一种基于随机森林算法的计及剩余电流扰动因子含光伏电源的低压配电台区漏电故障检测方法。通过多角度挖掘分析剩余电流扰动因子,利用剩余电流偏差法定量分析剩余电流扰动因子对剩余电流的影响,分析计及剩余电流扰动因子的漏电故障频域特性,构建了多维故障特征向量与特征数据集,建立了基于随机森林算法的漏电故障检测模型。通过仿真模型进行仿真分析与验证,结果表明所提方法可高精度检测漏电故障,与常用方法相比,所提方法的故障检测准确率和稳定性更高、抗干扰能力更强。

基于灰狼算法的配电网短路故障自动监测系统

针对接地短路故障中识别率较低、抗干扰性较大的问题,该文提出短路故障自动化监测系统。通过集成经验模态对零序电流做出分解,提取相应的本证模态分量,构建特征向量。结合灰狼算法对门控神经网络优化,加入相应的softmax分类优化,构建接地短路故障的自动化监测系统。经过实际算例验证,改进系统能够更稳定地进行故障识别,具有更高的准确率以及抗干扰性能。

基于差分光声光谱法的SF_(6)气体泄漏故障检测及预警

针对复合气体绝缘金属封闭开关(HGIS)设备在运行过程中出现的故障诊断以及SF_(6)气体泄漏故障检测效率低等问题,设计一种基于差分光声光谱法的气体检测方法。该方法通过利用分子红外吸收光谱技术,实现PPT及PPB量级的气体检测功能,避免空气自身光声效应的干扰。同时,通过构建设备状态空间模型,基于粒子滤波算法对卡尔曼滤波法进行扩展,比较状态变量的实时测量值与估计值,得到残差值并对系统故障进行预警。此外,加入自适应阈值方法检测设备故障,避免模型误差以及噪声的干扰造成误报警。实验结果显示,系统故障检测和故障预警效果较好,检测精度高于94%。

智能配电网中的线路监测与故障检测技术研究

电力系统的发展对线路监测与故障检测技术提出了新的要求。文章旨在研究智能配电网中的线路监测与故障检测技术,以提高电力系统的可靠性和智能化水平。介绍了智能配电网的组成与架构,明确了其在电力系统中的地位。回顾了传统的线路监测与故障检测技术,分析了其存在的局限性与不足。探讨了智能配电网中线路监测与故障检测技术的整合策略,包括数据集成与信息共享、算法融合与优化以及智能决策与响应机制。通过案例分析,验证了这些整合策略在实际项目中的应用效果。

一种改进的故障检测算法在组合导航中的应用

为提高组合导航系统的容错性能,实时检测滤波器处理测量信息的有效性,针对组合导航系统突变故障的检测问题,提出了相应的改进算法.该算法不仅对系统层面上进行故障探测,而且对故障进行定位和隔离,分析了作为假设检验量的标准化残差之间的相关性强弱对故障正确定位的影响,由此对基于标准化残差的故障检测与隔离算法进行了改进,实现子系统内部故障的正确定位与隔离.实验结果表明,改进的算法能够更有效地利用多余观测量,从而提高组合导航系统的稳健性.

基于遗传PNN网络的组合导航故障诊断研究

针对INS/GPS组合导航系统观测信息无冗余且观测信息可能存在故障的情形,提出一种基于状态χ2检验与自适应概率神经网络(APNN)相结合的组合导航故障诊断方法。该方法通过监测组合导航系统状态分量,提取故障的特征值并利用AP-NN确定故障的类别,从而实现故障的定位与隔离。APNN采用高斯函数作为激励函数,通过遗传算法获得模式层矢量最佳数目及匹配的平滑因子参数集,使每个神经元传递函数具有不同的平滑参数,提高了网络的泛化能力以及诊断精度。数值仿真结果表明,该算法能够有效地识别出INS/GPS组合导航系统的故障,确保了系统的安全性和可靠性。

组合导航系统故障检测的小波变换方法研究

为保证组合导航系统能始终正常工作 ,故障的检测与隔离至关重要。研究表明 ,小波变换方法在检测系统硬故障方面具有很大优势。本文即以 INS/ GNSS组合导航系统为例对此进行了仿真研究 ,并提出了两种具体的检测方案。通过仿真和分析 。

一种改进的残差χ^2检验方法在联邦UKF滤波器中的应用

故障检测和隔离是实现联邦UKF(unscented Kalman filter)滤波器工程应用的关键。针对常规残差χ2检验法因误警导致无故障子系统被频繁隔离的问题,提出了一种改进算法。该算法采用模糊逻辑和加权平均来处理残差χ2检验法的检验阈值,可以根据突变信号的大小自动调节检测时间的长短,特别是对不易检测的小幅值突变信号,自动延长检测时间从而利用更多的信息,有效降低了系统的误警率;在此基础上,进一步设计了合理的故障检测控制规则。将改进的残差χ2检验法应用于多传感器卫星组合导航系统中,仿真结果表明,改进算法有效提高了组合导航系统的稳定性,具有实用价值。

组合导航系统故障检测技术研究

提出了一种新的故障检测算法。该算法由联合滤波器的全局估计与各局部估计之差 ,得到残差序列 ,通过残差 χ2 检验 ,来判别系统是否出现了故障。同时进一步讨论了故障分离方法。数值仿真及实际应用结果表明 ,该算法能有效地检测出组合导航系统的故障 ,保证了系统的安全性和可靠性。

GNSS/SINS紧组合导航故障检测与系统重构新方法

针对传统新息χ~2检测法不能准确判断故障持续时间而造成的误警、漏警等问题,提出了一种基于层次滤波器结构的故障检测与系统重构新方法:利用新息检测法对各主、子滤波器进行故障检测,通过比较各滤波器检测结果识别出故障卫星号,将不包含故障星的子滤波器结果反馈到主滤波器及其他子滤波器,并代替其滤波结果对系统进行重构,进而辅助新息χ~2检测法进行故障检测。试验结果表明,在单星故障条件下,相比于传统新息χ~2检测法,新方法减弱了新息χ~2检测法的跟踪作用,实现了对故障星及故障时间的准确判定,提高了组合导航系统的完好性监测能力。

采用双状态传播卡方检验和模糊自适应滤波的容错组合导航算法

太阳能高空长航时无人机导航系统中,捷联惯导/北斗2κ全球卫星导航/星光导航(SINS/BD2/GPS/CNS)是一种可用的组合方案.针对常规容错组合导航算法故障检测类型单一,故障时滤波精度下降的问题,提出一种采用双状态卡方检验(TSPCST)和模糊自适应滤波(FAF)的容错组合导航算法.为了同时检测多种故障,将TSPCST应用于联邦滤波结构中;为了防止故障数据污染系统,利用FAF输出的高精度导航信息,对双状态传播器定期交替校正;进一步,FAF运用TSPCST检测得到的故障信息变量,定义量测子系统模糊有效域,将检测阈值模糊化,以弥补常规固定检测阈值算法难以选取阈值的不足;最后,通过计算信息分配因子,自适应处理多种故障数据.仿真结果表明,该容错组合导航算法性能优于常规固定检测阈值算法.