Multiple model-based detection and estimation scheme for gas turbine sensor and gas path fault simultaneous diagnosis

In this paper, a multiple model (MM)-based detection and estimation scheme for gas turbine sensor and gas path fault diagnosis is proposed, which overcomes the coupling effects between sensor faults and gas path faults, and simultaneously realizes an accurate diagnosis of sensor and gas path faults. First, an adaptive fault detection and isolation (FDI) framework based on the MM method was established to detect and isolate sensor faults and gas path faults. Then, a fault amplitude estimation method was proposed according to the FDI results, and a fault validation method based on the Chi-square test was proposed to confirm the actual fault. Finally, hardware in the loop (HIL) simulation platform was established to validate the effectiveness of the proposed method. Several simulation case studies were conducted based on a two-shaft marine gas turbine with common gas path faults and sensor faults. The simulation results show that the proposed method can accurately diagnose the fault and estimate the corresponding fault amplitude when both the sensor fault and the gas path fault coincide.

     

基于动态特征矩阵的k近邻风电机组故障检测方法

受风的间歇性和随机性影响风电机组运行状态频繁切换,导致设备状态异常检测误报和漏报情况严重,风电企业运维成本居高不下。为此,提出了基于动态特征矩阵的k近邻故障检测方法,该方法采用基于互信息的动态特征矩阵描述风电机组的动态特性,通过加权k近邻同时考虑动态特征矩阵中的特征贡献率与累计互信息的影响,利用动态阈值计算降低运行状态突变造成的误报。分别以美国可再生能源实验室5 MW海上风机基准模型的常见传感器和执行器故障以及SCADA数据中记录的变桨系统故障为例,将所提方法的故障检测结果分别与PCA、KPCA、FD-kNN以及PC-kNN故障检测方法进行对比,结果表明所提方法能够准确进行故障信息的检测,所提方法优于其他对比故障检测方法。     

Sensor fault detection and isolation system for a condensation process

This article presents the design of a sensor Fault Detection and Isolation (FDI) system for a condensation process based on a nonlinear model. The condenser is modeled by dynamic and thermodynamic equations. For this work, the dynamic equations are described by three pairs of differential equations which represent the energy balance between the fluids. The thermodynamic equations consist in algebraic heat transfer equations and empirical equations, that allow for the estimation of heat transfer coefficients. The FDI system consists of a bank of two nonlinear high-gain observers, in order to detect, estimate and to isolate the fault in any of both outlet temperature sensors. The main contributions of this work were the experimental validation of the condenser nonlinear model and the FDI system.     

基于神经网络的气体传感器故障诊断

该文介绍了一种基于人工神经网络进行了气体传感器故障检测的新方法，文中利用单个气体传感器的输出信息为气体传感器建立了动态非线性神经网络气体传感器输出模型，并利用该模型进行在线故障检测，实际使用证明该模型具有良好的收敛性和稳定性，完全能满足对气体传感器故障在线检测的需要。     

Isolability of faults in sensor fault diagnosis

A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.     

Sensor fault detection and isolation via high-gain observers: application to a double-pipe heat exchanger

This paper deals with fault detection and isolation (FDI) in sensors applied to a concentric-pipe counter-flow heat exchanger. The proposed FDI is based on the analytical redundancy implementing nonlinear high-gain observers which are used to generate residuals when a sensor fault is presented (as software sensors). By evaluating the generated residual, it is possible to switch between the sensor and the observer when a failure is detected. Experiments in a heat exchanger pilot validate the effectiveness of the approach. The FDI technique is easy to implement allowing the industries to have an excellent alternative tool to keep their heat transfer process under supervision. The main contribution of this work is based on a dynamic model with heat transfer coefficients which depend on temperature and flow used to estimate the output temperatures of a heat exchanger. This model provides a satisfactory approximation of the states of the heat exchanger in order to allow its implementation in a FDI system used to perform supervision tasks.     

Fault diagnosis and accommodation of a three-tank system based on analytical redundancy

This paper investigates the application of a fault diagnosis and accommodation method to a real system composed of three tanks. The performance of a closed-loop system can be altered by the occurrence of faults which can, in some circumstances, cause serious damage on the system. The research goal is to prevent the system deterioration by developing a controller that has some capabilities to compensate for faults, that is, the fault accommodation or fault-tolerant control. In this paper, a two-step scheme composed of a fault detection, isolation and estimation module, and a control compensation module is presented. The main contribution is to develop a unique structured residual generator able to isolate and estimate both sensor and actuator faults. This estimation is of paramount importance to compensate for these faults and to preserve the system performances. The application of this method to the three-tank system gives encouraging results which are presented and commented on in case of various kinds of faults.     

基于降维观测器的系统最优容错控制

针对含有传感器故障的线性连续系统,利用Riccati矩阵方程设计了故障情况下的动态最优容错控制律,并设计了能同时检测出系统状态和故障状态的增广的降维故障检测器,从而实现了系统的故障检测和容错控制并能满足二次型性能指标.仿真实例验证了这种方法简单有效.     

基于最小二乘支持向量机预测器的传感器故障检测与数据恢复

本文介绍了最小二乘支持向量机（LS-SVM）回归的基本原理，提出了一种基于LS-SVM回归的时间序列预测器，并将其用于传感器的故障检测和数据恢复。论述了LS-SVM预测器的实现方法和步骤，并且将其应用于压力传感器的故障检测和数据恢复，同线性神经网络预测器、RBF神经网络预测器和BP神经网络预测器的比较结果表明，LS-SVM预测器具有更高的预测精度，更好的外推能力，计算效率最高，因此，LS-SVM预测器是传感器故障检测和短期数据恢复的一种有效方法。     

Fault diagnosis for magnetic bearing systems

A full fault diagnosis for active magnetic bearing (AMB) and rotor systems to monitor the closed-loop operation and analyze fault patterns on-line in case any malfunction occurs is proposed in this paper. Most traditional approaches for fault diagnosis are based on actuator or sensor diagnosis individually and can solely detect a single fault at a time. This research combines two diagnosis methodologies by using both state estimators and parameter estimators to detect, identify and analyze actuators and sensors faults in AMB/rotor systems. The proposed fault diagnosis algorithm not only enhances the diagnosis accuracy, but also illustrates the capability to detect multiple sensors faults which occur concurrently. The efficacy of the presented algorithm has been verified by computer simulations and intensive experiments. The test rig for experiments is equipped with AMB, interface module (dSPACE DS1104), data acquisition unit MATLAB/Simulink simulation environment. At last, the fault patterns, such as bias, multiplicative loop gain variation and noise addition, can be identified by the algorithm presented in this work. In other words, the proposed diagnosis algorithm is able to detect faults at the first moment, find which sensors or actuators under failure and identify which fault pattern the found faults belong to.     

Model-based fault detection and isolation in steer-by-wire vehicle using sliding mode observer

Steer-by-Wire system (SbW), in which the conventional mechanical linkages between the steering wheel and the front wheel are removed, is suited to active steering control, improving vehicle stability, dynamics and maneuverability. And SbW is implemented to autonomous steering control to assist the driver. However, the SbW vehicle contains unsolved important problems about fault tolerant function. For example, it is the detection of sensor fault and multiplicative fault simultaneously. Fault detection and isolation (FDI) is essential in fault-tolerant problems, and conventional FDI for SbW was based on Kalman filter. But this method has weak robustness and cannot detect sensor fault and multiplicative fault simultaneously. We propose a novel model-based fault detection and isolation method using sliding mode observer in the SbW vehicle, which contains measurement of sensor fault and multiplicative fault. The effectiveness of the proposed method is verified by simulations. This paper was recommended for publication in revised form by Associate Editor Kyoungsu Yi Jae-Sung Im was born in Busan, Korea in 1978. He received his B.S. and M.S. degrees in Mechanical Engineering from Pukyong National University, Korea, in 2003 and 2005, respectively. He then received his Ph.D. degree from Kumamoto University, Japan, in 2009. His interests are in vehicle dynamics, robust control, fault detection and isolation, and man-machine interface. Fuminori Ozaki received the B.S. and M.S. degrees from the Department of Computer Science, Kumamoto University, Japan, in 1998 and 2000. In 2000, he joined OMRON Corporation, Kyoto, Japan, where he developed semiconductor manufacturing equipment. His current interests include EPS control and KANSEI engineering. Tae-Kyeong Yue received the B.S. and M.S. degrees from Pukyong National University, Korea, in 1998 and 2000, respectively. He received the Ph.D. degree from Kumamoto University, Kumamoto, Japan in 2003. He is working in the Korea Ocean Research and Development Institute (KORDI), Korea. His interests are fault detection and isolation, decentralized control and control of deep-sea mining system. Shigeyasu Kawaji received his Master of Engineering in Electrical Engineering and Doctor of Engineering in Control Engineering from Kumamoto University and Tokyo Institute of Technology, Japan, in 1969 and 1980, respectively. He joined the Department of Electronic Engineering of Kumamoto University, Japan, where he is presently as a full professor. He is the Director of System Integration Laboratory. He is presently the President of Advanced Health Laboratory Ltd. His current research interest includes robust control, intelligent control mechatronics and robotics, fusion of medicine and engineering, and automotive mechatronic systems.     

基于优化自适应阈值的非线性机电系统传感器 故障检测和主动容错控制

针对具有参数不确定性和传感器故障的非线性机电系统,提出一种基于优化自适应阈值和故障重构策略的主动容错控制方法。首先,利用线性分式变换理论对存在参数不确定性的非线性机电系统进行建模,并提出基于粒子群优化算法的优化自适应阈值以提高参数不确定条件下的故障检测性能。其次,通过解析冗余关系推导出系统的动力学方程,并提出一种基于递归终端滑模的跟踪控制策略,以实现系统健康状态下的负载位置跟踪。当系统发生故障时,构建自适应滑模观测器进行传感器故障重构,根据重构结果设计自适应主动容错控制律,并利用故障检测结果进行控制律的实时切换。实验结果表明,所提出的故障检测和主动容错控制方法能在0.06 s内准确的实现传感器故障检测和容错控制,验证了该方法的可行性。     

Quality monitoring of a closed-loop system with parametric uncertainties and external disturbances: a fault detection and isolation approach

Due to aging and environmental factors, system components may either fail or not function as expected, which causes unprecedented changes in the quality of the system. A timely detection of the onset of a fault in a component is crucial to a quality monitoring of a process if costly failures are to be avoided. However, finding the source of the failure is not trivial in systems with a large number of components and complex component relationships. In this paper, an efficient scheme to detect adverse changes in system reliability and find the failed component is proposed in order to have an effective process quality monitoring. The monitoring scheme has been made effective by implementing first the techniques of fixed-parameter Shewhart, MEWMA and Hotelling’s T² control chart, and then the adaptive versions of Shewhart Chart, MEWMA and T² control chart for counter checking the precision of quality reports. Once detected, the fault isolation scheme uses a Bayesian decision strategy based on the maximum correlation between the residual and one of a number of hypothesized residual estimates to generate a fault report. By doing so, the critical information about the presence or absence of a fault, and its isolation, is gained in a timely manner, thus making the quality monitoring system an effective tool for a variety of maintenance programs, especially of the preventive type. The proposed scheme is evaluated extensively on simulated examples, and on a physical fluid system exemplified by a benchmarked laboratory scale two-tank system to detect and isolate faults including sensor, actuator, and leakage ones.     

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach.     

银掺杂氧化锌基乙炔气体传感器的 检测特性研究

乙炔(C_2H_2)气体是运行电力变压器油中溶解的主要故障特征气体之一,能有效反映变压器的放电故障;气体检测传感器及其检测特性是实现故障气体在线分析的关键。提出一种金属银(Ag)掺杂氧化锌(ZnO)基的纳米传感器,基于实验室微量气体平台研究其对C_2H_2气体的检测特性及气敏机理。结果表明:由于Ag掺杂以后为ZnO表面引入了杂质能级,掺杂后的ZnO纳米传感器表现出比未掺杂更高的灵敏度和更快的响应特性,并保持良好的线性度和稳定性。该结果为高性能ZnO基C_2H_2气体传感器的研究提供了新思路。     

伺服电机控制系统中的电流检测硬件设计

现有的电流检测技术,大多采用模拟信号检测电流,存在保密性差、抗干扰能力弱等缺点,针对此现象设计了一种以DSP2808为核心的电流检测系统,采用闭环式霍尔电流传感器采集电机的电流信号,将检测到的电流信号按1：2000比例输出模拟信号,并将此模拟信号通过A／D转换器转换成数字信号,让数字信号隔离后传递给DSP来实现实时的电流检测。该电流检测系统经实验测试结果表明,具有高精度、误差小、噪声小、传输距离远等特点。     

热力参数的KPCA-RBF网络建模及传感器故障诊断方法

针对复杂恶劣环境下机组热力参数的数据监测及传感器故障诊断问题,建立了融合机理分析、核主元分析(kernel principle component analysis,简称KPCA)与径向基神经网络(radial basis function,简称RBF)的发电机组热力参数预测及传感器故障检测模型。首先,根据机理分析得到完备的辅助变量集,并利用核主元分析提取辅助变量的特征信息以有效处理发电机组中高维、强耦合的非线性数据;其次,将主元变量集输入径向基神经网络进行学习,实现热力参数的重构;最后,基于预测模型与窗口移动法实现传感器的故障诊断,并对故障数据进行及时修复和准确替换。以燃气轮机排气温度为例进行验证的结果表明,该预测模型具有更高的精度和泛化能力,能在传感器故障发生初期及时发现并识别故障类型,检测效果优良。     

楼宇空调水系统温度传感器固定偏差故障的诊断方法

针对HVAC水系统中温度传感器固定偏差的故障,提出了一种基于分析冗余和数理统计的故障诊断方法。该方法从能量守恒出发,通过选择不同控制体并建立相应守恒方程的残差表达式,利用数理统计方法将固定偏差故障的诊断问题转化为求解最小值问题,最终通过建立并求解诊断方程组获得传感器的固定偏差。还讨论了诊断所需数据样本的选取和诊断结果可靠性的判断。诊断方法在已建立的系统仿真器上进行了验证,结果表明该方法能够用于诊断由于漂移而引起的传感器故障。     

超声波远距离振动信号检测系统的设计

在机械设备状态监测与故障诊断中,振动信号是一个重要的状态参量。本文针对压电式、电涡流传感器等常规的振动测量方法和仪器在特殊环境中的局限性,提出了基于超声波的非接触式检测方法,研究并设计了系统检测装置。该检测装置主要由超声波发生模块、超声波接收模块和基于LabVIEW的数据采集/处理3个部分组成,利用该装置对振动对象进行远距离测量,并同时用传统测量仪器(电涡流位移传感器)作对照实验,得到较理想的检测结果,验证了该超声波振动检测装置的有效性和可行性。     

Multiple sensor fault diagnosis for dynamic processes

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology.