Sensor deployment based on fuzzy graph considering heterogeneity and multiple-objectives to diagnose manufacturing system

In this paper, a methodology is presented to generate an optimized sensor deployment deciding sensor types, numbers, and locations to accurately monitor fault signatures in manufacturing systems. Sensor deployment to robustly monitor operation parameters is the corner stone for diagnosing manufacturing systems. However, current literature lacks investigation in methodologies that handle heterogeneity among sensor properties and consider multiple-objective optimization involved in the sensor deployment. We propose a quantitative fuzzy graph based approach to model the cause–effect relationship between system faults and sensor measurements; analytic hierarchy process (AHP) was used to aggregate the heterogeneous properties of the sensor–fault relationship into single edge values in fuzzy graph, thus quantitatively determining the sensor's detectability to fault. Finally sensor–fault matching algorithms were proposed to minimize fault unobservability and cost for the whole system, under the constraints of detectability and limited resources, thus achieving optimum sensor placement. The performance of the proposed strategy was tested and validated on different manufacturing systems (continuous or discrete); various issues discussed in the methodology were demonstrated in the case studies. In the continuous manufacturing case study, the results illustrated that compared with signed directed graph (SDG), the proposed fuzzy graph based methodology can greatly enhance the detectability to faults (from SDG's 0.699 to fuzzy graph's 0.772). In the discrete manufacturing case study, results from different optimization approaches were compared and discussed; the detectability of sensors to faults also increased from SDG's 0.61 to fuzzy graph's 0.65. The two case study results show that the proposed approach overcame the qualitative approach such as signed directed graph's deficiency on handling sensor heterogeneity and multiple objectives; the proposed approach is systematic and robust; it can be integrated into diagnosis architecture to detect faults in other complex systems.     

Structural analysis of fault isolability in the DAMADICS benchmark

Structural analysis is a powerful tool for early determination of fault detectability/fault isolability possibilities. It is shown how different levels of knowledge about faults can be incorporated in a structural fault isolability analysis and how they result in different isolability properties. The results are evaluated on the DAMADICS valve benchmark model. It is also shown how to determine which faults in the benchmark need further modelling to get desired isolability properties of the diagnosis system.     

Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications

The battery sensors fault diagnosis is of great importance to guarantee the battery performance, safety and life as the operations of battery management system (BMS) mainly depend on the embedded current, voltage and temperature sensor measurements. This paper presents a systematic model-based fault diagnosis scheme to detect and isolate the current, voltage and temperature sensor fault. The proposed scheme relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation. Structural analysis handles the pre-analysis of sensor fault detectability and isolability possibilities without the accurate knowledge of battery parameters, which is useful in the early design stages of diagnostic system. It also helps to find the analytical redundancy part of the battery model, from which subsets of equations are extracted and selected to construct diagnostic tests. With the help of state observes and other advanced techniques, these tests are ensured to be efficient by taking care of the inaccurate initial State-of-Charge (SoC) and derivation of variables. The residuals generated from diagnostic tests are further evaluated by a statistical inference method to make a reliable diagnostic decision. Finally, the proposed diagnostic scheme is experimentally validated and some experimental results are presented.     

Sensor placement for fault isolation in linear differential-algebraic systems

An algorithm is proposed for computing which sensor additions make a diagnosis requirement specification regarding fault detectability and isolability attainable for a given linear differential-algebraic model. Restrictions on possible sensor locations can be given, and if the diagnosis specification is not attainable with any available sensor addition, the algorithm provides the solutions that maximize specification fulfillment. Previous approaches with similar objectives have been based on the model structure only. Since the proposed algorithm utilizes the analytical expressions, it can handle models where structural approaches fail.     

基于小波分析的传感器故障诊断方法仿真研究

针对装甲车辆数据采集控制系统中传感器故障难于直接检测的问题,本文提出一种基于小波变换的方法对传感器故障进行诊断。首先介绍了小波分析的基本理论,对比阐述了连续小波变换和离散小波变换的原理和优缺点;其次利用Matlab/Simulink搭建模型进行仿真试验,采用连续小波变换对传感器故障进行检测,准确定位故障发生时刻;最后本文还选取了几种典型的传感器故障进行诊断,仿真结果验证了该方法的可行性和准确性。     

基于自适应观测器的四旋翼无人飞行器传感器故障诊断方法

针对四旋翼无人飞行器传感器故障诊断问题,提出一种用于四旋翼无人飞行器加速度计和陀螺仪故障同时发生的故障检测与隔离以及故障偏差值估计的非线性诊断方法.首先,在建立飞行器动力学模型和传感器模型的基础上,构建四旋翼无人飞行器传感器故障检测与诊断系统.其次,利用故障观测器完成传感器故障的检测与隔离,基于Laypunov方法设计非线性自适应观测器对未知故障偏差值进行估计.最后,在传感器测量噪声存在的情况下,证明自适应律的稳定性和参数收敛性.实验结果表明,该方法能有效进行传感器的故障检测与隔离,实现对传感器故障偏差的估计与跟踪.     

Fault detection in linear discrete dynamic systems by a reduced order generalized-likelihood-ratio method

The detectability by conventional step-hypothesized generalized-likelihood-ratio (SHGLR) method for detection of a parameter change (fault detection) in a linear discrete dynamic system is analysed and it is shown that a weakly-diagnosable-space (WDS) exists for dynamics and sensor faults. Based on the fault detectability, a reduced order SHGLR method is then developed which highly improves the detection rate and speed. In the same framework of the GLR method, another reduced order detection scheme is given, which makes the most use of the information about the input and the state of the system to raise the detectability for faults for the case where the step hypothesis cannot be applied effectively.     

基于自适应观测器的四旋翼无人飞行器多传感器故障诊断方法

为了实现对四旋翼无人飞行器多传感器故障检测与诊断,提出一种基于自适应观测器的多传感器故障诊断方法。首先,在建立飞行器动力学模型和传感器模型的基础上,将传感器故障视为虚拟执行器故障,构建四旋翼无人飞行器多传感器故障检测与诊断系统;其次,设计非线性观测器实现多故障检测和与隔离,基于Laypunov方法设计非线性自适应观测器实现对多故障偏差值的估计;最后,在传感器测量噪声存在的情况下,证明自适应律的稳定性和参数收敛性。实验结果表明,该方法能有效进行多传感器的故障检测与隔离,实现对多传感器故障偏差的同时估计与跟踪。     

灰色动态预测在AUV传感器故障诊断中的应用

针对自主水下机器人(AUV)传感器故障诊断中样本数据少、随机性强、实时性要求高的特点,将灰色动态预测模型的建模原理引用到AUV传感器的故障诊断中。在对传感器进行数据滤波、小样本灰色建模与灰色动态预测的基础上,可以实现AUV传感器的实时故障诊断。文章详细阐述了基于灰色动态预测的传感器故障诊断的具体实现方法和步骤,对AUV传感器中典型的四种故障模式进行了仿真研究。结果表明该方法能快速、准确地诊断出传感器故障,并且在传感器发生故障后的一段时间内能够实现信号恢复。     

Design of nonlinear observers for detecting faults in hydraulic sub-sea pipelines

Firstly, a general nonlaminar model is considered for pipeline dynamics, including a treatment of faults caused by pipe restrictions. For three cases results are given for stability, robustness and fault detectability of a combined observer and residual (fault detection signal). An efficient numerical design algorithm is proposed. The method is applied to an actual experimental pipeline (rig system) which is set up to model a sub-sea umbilical. Results on modelling and on observer and residual (signal) design are given. The effectiveness of the design is tested by inducing two types of fault on the rig system.     

基于故障映射向量和结构化残差的主元分析(PCA)故障隔离

在基于主元分析(PCA)的多变量统计过程监控中, 现有方法很难直观有效地完全实现故障的隔离与诊断. 本文通过分析各类故障的数学模型, 提出一种基于结构化残差和故障映射向量的隔离方法, 并推导出PCA模型下过程故障映射向量方向的提取算法, 进而实现了传感器/执行器故障和过程故障的故障隔离, 在CSTR仿真上的研究进一步验证了该法的有效性.     

Current-sensor fault detection and isolation for induction-motor drives using a geometric approach

This work presents the design of a current-sensor fault detection and isolation system for induction-motor drives. A differential geometric approach is addressed to determine if faults can be detected and isolated in drives with two line current sensors by using a model based strategy. A set of subsystems is obtained based on the observability co-distribution, whose outputs are decoupled from the load torque (detectability) and only affected by one of the sensors (isolability). A bank of observers is designed for these subsystems in order to obtain residuals for the fault detection and isolation. It is demonstrated that the proposed strategy allows detecting single and multiple sensor faults, including disconnection, offset and gain faults. Experimental results validate the proposal.     

Fault Detection Schemes for Continuous-Time Stochastic Dynamical Systems

In this paper the fault detection (FD) task in stochastic continuous-time dynamical systems is addressed. A new family of FD approaches is presented, which is based on the application of hypothesis testing on continuous-time estimators. The given FD schemes are widely analyzed in the framework of their characteristics, such as fault detectability, false alarms and missed detection. A collection of sufficient detectability conditions are given for a class of faults (referred here as generic), characterizing the faults which can be detected with certain formalized guarantee by the given FD schemes, and providing also an upper bound for the detection time in a probabilistic sense. The application and comparative performance of these FD approaches is illustrated for different faults in a simulation example.     

Integration of fault diagnosis and control based on a trade-off between fault detectability and closed loop performance

This paper presents a novel methodology for simultaneous optimal tuning of a fault detection and diagnosis (FDD) algorithm and a feedback controller for a chemical plant in the presence of stochastic parametric faults. The key idea is to propagate the effect of time invariant stochastic uncertainties onto the measured variables by using a Generalized Polynomial Chaos (gPC) expansion and the nonlinear first principles’ model of the process. A bi-level optimization is proposed for achieving a trade-off between the fault detectability and the closed loop process variability. The goal of the outer level optimization is to seek a trade-off between the efficiency of detecting a fault and the closed loop performance, while the inner level optimization is designed to optimally calibrate the FDD algorithm. The proposed method is illustrated by a continuous stirred tank reactor (CSTR) system with a fault consisting of stochastic and intermittent variations in the inlet concentration. Beyond achieving improved trade-offs between fault detectability and control, it is shown that the computational cost of the gPC model based method is lower than the Monte Carlo type sampling based approaches, thus demonstrating the potential of the gPC method for dealing with large problems and real-time applications.     

Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy

One of the most critical issues when deploying wireless sensor networks for long-term structural health monitoring (SHM) is the correct and reliable operation of sensors. Sensor faults may reduce the quality of monitoring and, if remaining undetected, might cause significant economic loss due to inaccurate or missing sensor data required for structural assessment and life-cycle management of the monitored structure. This paper presents a fully decentralized approach towards autonomous sensor fault detection and isolation in wireless SHM systems. Instead of physically installing multiple redundant sensors in the monitored structure (“physical redundancy”), which would involve substantial penalties in cost and maintainability, the information inherent in the SHM system is used for fault detection and isolation (“analytical redundancy”). Unlike traditional centralized approaches, the analytical redundancy approach is implemented distributively: Partial models of the wireless SHM system, implemented in terms of artificial neural networks in an object-oriented fashion, are embedded into the wireless sensor nodes deployed for monitoring. In this paper, the design and the prototype implementation of a wireless SHM system capable of autonomously detecting and isolating various types of sensor faults are shown. In laboratory experiments, the prototype SHM system is validated by injecting faults into the wireless sensor nodes while being deployed on a test structure. The paper concludes with a discussion of the results and an outlook on possible future research directions.     

相位差结合小波包的无人直升机故障诊断

针对低成本的微小型无人直升机（MUH）传感器性能不稳定,容易出现故障的缺陷,提出了一种基于相位差和小波包分析相结合的故障诊断方法。根据MUH传感器输出信号的特点,建立了基于相位差的故障诊断模型,利用相关分析法估计相位差进行故障检测,采用小波阈值法对采样信号进行预处理,以提高相位差的估计精度,运用小波包分析进行故障分离。结合实验数据进行仿真,结果表明该方法是一种行之有效的MUH传感器故障诊断方法,已成功应用在某微小型无人直升机的飞行实验中。     

离散时间线性时变系统的传感器故障估计滤波器设计

针对一类离散时间线性时变系统提出了一种传感器故障诊断方法.本文首先通过状态增广的方式将被研究的系统转化为描述系统的形式,并且基于该描述系统模型,采用方差最小化原则设计了一种能够同时估计系统状态和传感器故障的故障估计滤波器,然后利用一组故障估计滤波器提出了一种故障诊断方法.本文的主要贡献在于针对离散线性时变系统提出了一种不需要对故障动态进行假设的传感器故障诊断方法.所提出方法的另一个优点是该方法能够在存在过程和测量噪声的情况下实现故障检测、分离与估计.仿真结果说明了所提出方法的有效性.     

Control‐oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

As the solid oxide fuel cell (SOFC) system work environment is a high‐temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer‐based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault.     

Variable reconstruction and sensor fault identification using canonical variate analysis

The detection and identification of faults in dynamic continuous processes has received considerable recent attention from researchers in academia and industry. In this paper, a canonical variate analysis (CVA)-based sensor fault detection and identification method via variable reconstruction is described. Several previous studies have shown that CVA-based monitoring techniques can effectively detect faults in dynamic processes. Here we define two monitoring indices in the state and noise spaces for fault detection and, for sensor fault identification, we propose three variable reconstruction algorithms based on the proposed monitoring indices. The variable reconstruction algorithms are based on the concepts of conditional mean replacement and object function minimization. The proposed approach is applied to a simulated continuous stirred tank reactor and the results are compared to those obtained using the traditional dynamic monitoring technique, dynamic principal component analysis (PCA). The results indicate that the proposed methodology is quite effective for monitoring dynamic processes in terms of sensor fault detection and identification.