空调系统中传感器故障检测与诊断方法的研究

提出了空调系统传感器故障检测，故障识别、故障重构的主成分分析方法。主成分分析法将测量空间分为主成分子空间和残差子空间。SPE指数和SVI指标分别用来检测和识别故障，沿着故障方向，测量数据逐步逼近主成分子空间可以实现数据的重构，通过对空调监测系统的传感器故障检测与诊断结果展示出PCA方法具有良好的故障检测，识别和重构建能力。     

多传感器系统中基于扩展卡尔曼滤波器的在线故障检测

随着多传感器系统的广泛应用,在线故障对于系统性能影响严重,如何使得多传感器系统具有自主故障检测与诊断能力成为首要问题。根据非线性多传感器系统的输入信号、输出信号和故障阵列,建立一种具有多输入多输出处理和自调节加强功能的扩展卡尔曼滤波器（ EKF）的故障分析模型,在此基础上,提出了一种适用于多传感器系统的在线故障检测算法及其在传感器节点上的实施架构。实验结果表明：所提算法在低并发故障和高并发故障环境下均具有高准确度故障报告能力。此外,在温度传感器上实施所提算法,温度监测值的对比结果验证了所提算法比传统算法具有更好的系统性能保证能力。     

具有容错能力的锅炉液位传感器

依据现有锅炉液位检测的现状,设计出了一种数字式锅炉液位传感器。该传感器利用单片机的并口测量液位数据,串口与上位机进行通信,并能对传感器的故障进行诊断。经现场试运行表明:该传感器系统设计合理、可行,具有一定的容错能力。为系统维护及故障查找提供了方便,具有较高的性价比。     

基于x2检验的FADS系统故障检测与管理技术研究

嵌入式大气数据传感系统采用分布在飞行器前端周线上的压力传感器阵列测量的压力推导大气参数,这种多测压孔结构的系统,比传统的探针插入式大气数据传感器具有较强的抗扰动能力,但是若把故障的压力测量信息引入到迭代算法会导致运算发散甚至整个系统失效;针对这一情况,提出利用卡方分析来检查测压点故障的方法,一旦确定测压点故障,便将该点排除出运算法则;仿真结果表明,该系统具备一定的容错能力.     

一种基于容错的感知数据回归模型研究

传感器主要用于对外部环境进行监测,然而当传感器发生故障时监测结果会出现误差。为了提高传感器发生故障时系统的容错能力,提出了一种容错的感知数据回归模型。首先,对最小二乘和岭回归两种线性回归模型进行分析,并分析了线性回归模型的相关统计量;然后,分析了部分传感器发生故障时系统的相关统计量,并以此为基础分析了协变量矩阵的上下界;最后,依据协变量矩阵定义了故障指标,并将优化模型转化为同时最小化故障指标和均方误差的问题。实验表明,提出的容错回归模型与传统的最小二乘法和岭回归方法相比具有更小的预测误差,因而当传感器发生故障时所提模型具有更好的健壮性。     

基于Х^2检验的FADS系统故障检测与管理技术研究

嵌入式大气数据传感系统采用分布在飞行器前端周线上的压力传感器阵列测量的压力推导大气参数。这种多测压孔结构的系统，比传统的探针插入式大气数据传感器具有较强的抗扰动能力，但是若把故障的压力测量信息引入到迭代算法会导致运算发散甚至整个系统失效；针对这一情况，提出利用卡方分析来检查测压点故障的方法。一旦确定测压点故障，便将该点排除出运算法则；仿真结果表明，该系统具备一定的容错能力。     

基于小波神经网络的空调系统传感器故障诊断

详细阐述了小波神经网络(WNN)的原理、结构,并对传统的BP算法进行了改进。以空调系统传感器故障检测问题为目标,提出了基于WNN的故障诊断方法。通过采集天津博物馆中的传感器数据,对训练好的WNN进行了传感器故障诊断能力的验证,对温度传感器的1℃偏差故障、0.05℃/s速率漂移故障、完全故障、与不同方差下的精度等级下降故障进行了仿真,结果表明:这种方法对传感器故障具有很好的诊断效果。     

基于主元分析法的航空发动机传感器故障诊断研究

主要研究了主元分析方法在航空发动机传感器故障诊断中的应用,并提出了主元分析法故障诊断算法。假设只有传感器故障情况下,将传感器测量值所组成的测量空间分解为主元和残差两个子空间,并通过传感器实际测量数据与正常数据矩阵在残差空间上的投影做比较,对传感器故障进行故障诊断;针对航空发动机的压力温度转速等传感器常见的故障,通过运行故障仿真平台绘制了其多元统计特征图;分析仿真结果表明,主元分析法对航空发动机传感器具有很好的故障检测和故障诊断能力。     

BP神经网络在飞控系统传感器故障诊断中的应用

故障检测和诊断技术对提高系统可靠性具有重要意义,针对飞控系统中常见的传感器故障,提出了基于神经网络观测器的故障诊断方法;通过构造神经网络模型代替解析系统建模,利用神经网络的学习能力在线检测传感器故障,最后,应用BP神经网络算法对故障进行仿真;仿真结果表明,神经网络观测器方法对单一传感器故障及多个传感器故障均能够准确识别,并对故障的定位也有不错的效果。     

基于定性趋势分析的空调系统传感器故障检测

提出了基于定性趋势分析的空调系统传感器故障检测方法。该方法将空调系统中信号相似的传感器分成一组,利用组中信号间的趋势相似性进行故障检测。采集了空调系统的传感器数据,对传感器偏置故障和漂移故障进行仿真实验,结果表明,该方法能检测传感器的偏置故障和漂移故障。     

基于联合神经网络的冗余传感器故障诊断和信号重构

本文提出一种基于联合神经网络的传感器故障诊断和信号重构的方法．联合神经网 络的初级神经网络实现冗余信息的压缩，利用冗余信息把故障信息过滤掉，第二级把压缩后 的非故障信息复原，然后通过SPE图来诊断故障．发现故障后利用冗余信息实现信号重构．     

基于神经网络的航空传感器故障检测

用离线训练的神经网络进行导航传感器故障检测。首先,用已获得的正常飞行数据通过离线训练的方法训练神经网络并构造估计器的结构,然后用已选择好结构并训练好的神经网络作为估计器对传感器的读数进行一步预测。若预测值与传感器实际值之间的差值仅为递推误差和传感器输出噪声,则认为传感器工作正常,若相应的残差分量显著增大,则认为传感器故障。因此设计了相应的检测策略进行故障检测,以达到既避免不必要的报警、切换,又准确、及时的监测、报警。通过仿真试验验证,结果证明该方法可行。     

深空探测航天器姿态控制系统故障定位

执行机构与敏感器故障检测与定位是深空探测任务卫星平台可靠运行的前提和保障.本文从数据的角度出发,结合姿控系统工作机理,提出一种基于神经网络和支持向量机结合的故障诊断方法用于检测并定位故障.故障诊断方法分为3步,首先采集姿控系统的状态信息,采用神经网络对闭环姿控系统中未知动态特性建模并进行预测;然后将姿控系统敏感器信号与神经网络预测输出比较生成残差并提取故障特征;最后采用支持向量机辨识残差特征检测故障,并结合运动学特性分析定位故障.仿真结果表明本文所提方法可以有效提取、辨识故障特征,实现执行器与敏感器的故障检测定位.     

一种神经网络预测器在传感器故障诊断中的应用

讨论基于神经网络预测器的传感器故障诊断问题.介绍了传感器故障诊断技术的发展,提出了一种基于神经网络在线学习的传感器故障实时诊断的模型.通过比较三种前馈神经网络的预测残差确定网络类型.介绍了网络的学习规则,说明了在线学习的能力.最后,通过电厂高加热器的几个温度传感器的实际数据为例说明了此方法的实效性.     

Fault diagnosis of power electronic system based on fault gradation and neural network group

We propose a new fault diagnosis approach with fault gradation using BP (back-propagation) neural network group consisting of 3 sub BP neural networks. According to the hazard extents and the occurrence frequencies of different faults, the faults are divided into different grades. The higher the fault grade, the larger the number of the used sub neural networks is. Experimental results show that our approach makes the correctness rate of the fault diagnosis rise greatly (from less than 95.0% to 99.5%) and the performance of the whole fault diagnosis system gets much better especially for the on-line complex systems. The approach proposed in this paper also can be extended to other complex fault diagnosis systems, such as mechanical systems.     

Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network

In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.     

基于神经网络的非线性系统故障检测及容错控制方法

利用神经网络的非线性建模能力，提出了一种非线性系统的故障检测及容错控制方法。在本方法中，首先应用神经网络设计故障估计器，在线估计系统故障向量，实现故障检测；在此基础上，引入补偿控制器，消除故障对系统运行的影响，从而实现容错控制。同时基于Ｌｙａｐｕｎｏｖ方法进行了稳定性分析。     

A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.     

A Survey of Fault Management in Wireless Sensor Networks

Wireless sensor networks are resource-constrained self-organizing systems that are often deployed in inaccessible and inhospitable environments in order to collect data about some outside world phenomenon. For most sensor network applications, point-to-point reliability is not the main objective; instead, reliable event-of-interest delivery to the server needs to be guaranteed (possibly with a certain probability). The nature of communication in sensor networks is unpredictable and failure-prone, even more so than in regular wireless ad hoc networks. Therefore, it is essential to provide fault tolerant techniques for distributed sensor applications. Many recent studies in this area take drastically different approaches to addressing the fault tolerance issue in routing, transport and/or application layers. In this paper, we summarize and compare existing fault tolerant techniques to support sensor applications. We also discuss several interesting open research directions. Lilia Paradis is currently a graduate student in the Department of Mathematical and Computer Sciences, Colorado School of Mines. She is also part of the Toilers Ad Hoc Networking research group. She is interested in distributed communication protocols for wireless sensor networks. Qi Han received the PhD degree in computer science from the University of California, Irvine in 2005. She is currently an assistant professor in the Department of Mathematical and Computer Sciences, Colorado School of Mines. Her research interests include distributed systems, middleware, mobile and pervasive computing, systems support for sensor applications, and dynamic data management. She is specifically interested in developing adaptive middleware techniques for next generation distributed systems. She is a member of the IEEE and the ACM.     

SFDIA of consecutive sensor faults using neural networks – demonstrated on a UAV

Neural network based sensor fault detection, isolation and accommodation (NN-SFDIA) is becoming a popular alternative to traditional linear time-invariant model-based sensor fault detection, isolation and accommodation (SFDIA) schemes, such as observer-based methods. Their online training capabilities and ability to model complex nonlinear systems have attracted much research interest in the applications area of neural networks. In this article, we design an NN-SFDIA scheme to detect multiple sensor faults in an unmanned air vehicle (UAV). Model-based SFDIA is a direction of development in particular with UAVs where sensor redundancy may not be an option due to weight, cost and space implications. In this article, a maximum of three consecutive faults are assumed in the pitch gyro, normal accelerometer and angle of attack sensor of a nonlinear UAV model. Furthermore, a novel residual generator which is designed to minimise the false alarm rates and missed faults, is implemented. After 33 separate SFDIA tests implemented on a 1.6?GHz Pentium processor, the NN-SFDIA scheme detected all but three faults with a fast execution time of 0.55?ms per flight data sample.