Hybrid Fault Diagnosis and Isolation for Component and Sensor of APU in a Distributed Control System

This paper addresses the gas path component and sensor fault diagnosis and isolation(FDI) for the auxiliary power unit(APU). A nonlinear dynamic model and a distributed state estimator are combined for the distributed control system. The distributed extended Kalman filter(DEKF)is served as a state estimator,which is utilized to estimate the gas path components’ flow capacity. The DEKF includes one main filter and five sub-filter groups related to five sensors of APU and each sub-filter yields local state flow capacity. The main filter collects and fuses the local state information,and then the state estimations are feedback to the sub-filters. The packet loss model is introduced in the DEKF algorithm in the APU distributed control architecture. FDI strategy with a performance index named weight sum of squared residuals(WSSR) is designed and used to identify the APU sensor fault by removing one sub-filter each time. The very sensor fault occurs as its performance index WSSR is different from the remaining sub-filter combinations. And the estimated value of the soft redundancy replaces the fault sensor measurement to isolate the fault measurement. It is worth noting that the proposed approach serves for not only the sensor failure but also the hybrid fault issue of APU gas path components and sensors. The simulation and comparison are systematically carried out by using the APU test data,and the superiority of the proposed methodology is verified.

Fault Detection and Isolation in Industrial Systems Based on Spectral Analysis Diagnosis

The diagnoses in industrial systems represent an important economic objective in process industrial automation area. To guarantee the safety and the continuity in production exploitation and to record the useful events with the feedback experience for the curative maintenance. We propose in this work to examine and illustrate the application ability of the spectral analysis approach, in the area of fault detection and isolation industrial systems. In this work, we use a combined analysis diagram of time-frequency, in order to make this approach exploitable in the proposed supervision strategy with decision making module. The obtained results, show clearly how to guarantee a reliable and sure exploitation in industrial system, thus allowing better performances at the time of its exploitation on the supervision strategy.

Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems.Actuator and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be determined.The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

Multiobjective fault detection and isolation for flexible air-breathing hypersonic vehicle

An application of the multiobjective fault detection and isolation（FDI） approach to an air-breathing hypersonic vehicle（HSV） longitudinal dynamics subject to disturbances is presented.Maintaining sustainable and safe flight of HSV is a challenging task due to its strong coupling effects,variable operating conditions and possible failures of system components.A common type of system faults for aircraft including HSV is the loss of effectiveness of its actuators and sensors.To detect and isolate multiple actuator/sensor failures,a faulty linear parameter-varying（LPV） model of HSV is derived by converting actuator/system component faults into equivalent sensor faults.Then a bank of LPV FDI observers is designed to track individual fault with minimum error and suppress the effects of disturbances and other fault signals.The simulation results based on the nonlinear flexible HSV model and a nominal LPV controller demonstrate the effectiveness of the fault estimation technique for HSV.

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.

Performance of the geometric approach to fault detection and isolation in SISO,MISO,SIMO and MIMO systems

In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output （SISO）, Multiple-Input Single-Output （MISO）, and Single-Input Multiple-Output （SIMO） cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX （Auto Regressive with exogenous input） and VARX （Vector ARX） models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs, the classification errors reduce, even in the case of applying only one of the inputs in the computations. Furthermore, increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also, it is shown that by using probabilistic space in the distance function, fault diagnosis scheme has better performance in comparison with the deterministic one.

Fault diagnosis and isolation of the componentand sensor for aircraft engine

Aircraft engine component and sensor fault detection and isolation approach was proposed,which included fault type detection module and component-sensor simultaneous fault isolation module.The approach can not only distinguish among sensor fault,component fault and component-sensor simultaneous fault,but also isolate and locate sensor fault and the type of engine component fault when the engine component fault and the sensor faults occur simultaneously.The double-threshold mechanism has been proposed,in which the fault diagnostic threshold changed with the sensor type and the engine condition,and it greatly improved the accuracy and robustness of sensor fault diagnosis system.Simulation results show that the approach proposed can diagnose and isolate the sensor and engine component fault with improved accuracy.It effectively improves the fault diagnosis ability of aircraft engine.

A gain-varying UIO approach with adaptive threshold for FDI of nonlinear F16 systems

A discrete gain-varying unknown input observer （UIO） method is presented for actuator fault detection and isolation （FDI） problems in this paper. A novel residual scheme together with a moving horizon threshold is proposed. This design methodology is applied to a nonlinear F16 system with polynomial aerodynamics coefficient expressions, where the coefficient expressions for the F16 system and UIOs may be slightly different. The simulation results illustrate that a satisfactory FDI performance can be achieved even when the F16 system is under the environment of model uncertainties, exogenous noise and measurement errors.

基于数字孪生和IETM的装备常规故障诊断

装备在实际运行过程中出现的大部分常规故障都是由于人为操作、保养以及环境等因素引起的。常规故障在装备的研制、试验、鉴定以及维保过程中都进行了验证,并有明确的定义,具有明确的故障树分析结论,因而数字孪生可为装备常规故障提供快速、交互性的诊断可能。为提高常规故障诊断的效率和诊断过程的交互式体验感,提出一种基于数字孪生的装备常规故障诊断模型。在此基础上,鉴于交互式电子技术手册(IETM)良好的交互性及用户体验感,设计一种基于孪生数据和IETM的装备常规故障诊断方案。该方案将装备故障树转换为IETM的故障数据模块,存储到IETM数据库中形成故障库。采用故障树分析方法并将装备实时孪生数据作为输入,利用IETM的交互性,通过过程数据模块结合故障数据模块进行步进式故障诊断及隔离引导操作,同时提供维修操作指导。当装备实时孪生数据与故障库定义的故障信息不完全一致时,采用案例分析法计算装备实时孪生数据提供的故障征兆信息与故障库中明确定义的故障征兆信息的相似度,并设定相似度的阈值进行判定;再利用IETM推送对应故障数据模块进行相应的故障诊断隔离引导。

Sequential Fault Diagnosis Using an Inertial Velocity Differential Evolution Algorithm

The optimal test sequence design for fault diagnosis is a challenging NP-complete problem.An improved differential evolution(DE)algorithm with additional inertial velocity term called inertial velocity differential evolution(IVDE)is proposed to solve the optimal test sequence problem(OTP)in complicated electronic system.The proposed IVDE algorithm is constructed based on adaptive differential evolution algorithm.And it is used to optimize the test sequence sets with a new individual fitness function including the index of fault isolation rate(FIR)satisfied and generate diagnostic decision tree to decrease the test sets and the test cost.The simulation results show that IVDE algorithm can cut down the test cost with the satisfied FIR.Compared with the other algorithms such as particle swarm optimization(PSO)and genetic algorithm(GA),IVDE can get better solution to OTP.

一种发动机热态衰减阶段的故障诊断集成学习方法

针对汽车发动机在热态衰减阶段的故障诊断问题,提出了基于隔离森林算法和随机森林算法的故障诊断集成学习方法,隔离森林算法通过构建孤立树来识别异常点,随机森林算法构建多个决策树并综合它们的预测结果来进行异常分类。实验结果表明,融合隔离森林和随机森林算法的集成学习模型比传统的基线模型在发动机数据异常检测和分类的故障检测任务中具备更高的准确性、鲁棒性和应用潜力。

Visual Perception-Based Fault Diagnosis in Froth Flotation Using Statistical Approaches

Froth flotation is an important mineral concentration technique.Faulty conditions in flotation processes may cause the huge waste of mineral resources and reagents,and consequently,may lead to deterioration in terms of benefits of flotation plants.In this paper,we propose a computer vision-aided fault detection and diagnosis approach for froth flotation.Specifically,a joint Gabor texture feature based on the Copula model is designed to describe froth images;a rejection sampling technique is developed to generate training sets from the quality distribution of real flotation products,and then an isolation forest-based fault detector is learned;and a fault diagnosis model based on spline regression is developed for root cause identification.Simulation experiments conducted on the historical industry data show that the proposed strategy has better performance than the alternative methods.Thereafter,the entire framework has been tested on a lead-zinc flotation plant in China.Experimental results have demonstrated the effectiveness of the proposed method.

配电网故障隔离与自愈技术研究

文章以配电网为研究中心,分析其故障隔离与自愈技术。从故障诊断、故障定位及隔离方式3个角度探讨配电网故障隔离方法,在此基础上以故障自愈场景分析、系统设计和实现等为重点,研究配电网故障自愈技术。最后结合计算机模拟实验,探讨上述技术和方法的应用优势,以系统呈现现代技术在配电网故障隔离与自愈管理中的价值,服务未来配电网管理。

闭环系统故障诊断技术综述

本文着眼于闭环环境下动态系统中的故障诊断问题.介绍了目前闭环系统故障诊断的研究意义,特别关注了开闭环故障诊断的不同,并概述了现有的闭环系统故障诊断方法.通过仿真实验比较了典型开环系统和闭环系统中故障诊断的不同性能.最后探讨了闭环系统故障诊断研究中亟待解决的问题以及未来可能的研究方向.

复合故障诊断技术综述

复合故障诊断是故障诊断技术中的难点问题,因为实际工程和复杂系统中多个故障经常存在.本文对复合故障的故障机理、表现形式、诊断思路进行了概述.将故障模式识别存在多输出可能性的求解问题定义为复合故障诊断的科学问题.分析了复合故障诊断的难点.讨论了复合故障的研究现状,按照基于解析模型、基于定性经验、数据驱动的分类方式分别对现有的典型诊断方法和相关技术进行了综述,指出了这些方法的适用范围和优缺点.并探讨了复合故障诊断技术的发展方向.

基于多信号模型的故障诊断策略设计

对基于多型号模型的故障诊断策略设计进行了深入研究,介绍了多信号模型的定义和表示,提出了在单故障假设及不考虑组成单元可靠性、测试时间和测试费用影响下的故障检测用测试和故障隔离用测试优选算法以及故障诊断策略制定方法,并结合示例说明了诊断策略的设计过程;文中所提出的诊断策略设计方法能够以尽可能少的测试完成故障检测和隔离,按此方法设计的诊断策略具有自适应特性,适用于各类系统和设备的测试性分析与设计和故障诊断。

基于多信号模型的测试性分析与故障诊断策略设计

开展测试性分析与故障诊断策略设计是测试性设计工作的重要组成部分,能否通过测试性分析设计出最优的故障诊断策略直接影响着系统故障检测能力和故障隔离能力的提高。首先对某型导弹装备搜索雷达天线稳定系统的组成原理及工作流程进行了介绍,接着针对该系统诊断能力不足,不能对故障进行隔离的缺点,运用基于多信号模型TEAMS软件系统对天线稳定系统进行了测试性建模,并对系统的测试性进行了分析,最后设计出了合理有效的故障诊断策略。

无线传感器网络云连接紧急行为检测技术研究

在无线传感器网络云连接中,安全本身涉及到对内部或外部恶意企图的检测和防御。通常情况下,一种异常行为会导致后续警报频发,因而一旦发现无线传感器网络中存在异常行为,就必须要对信息进行管理。文章描述了美国国防部高级研究计划局(DARPA)信息生存能力研究内容,分析了信息报告与管理模型,介绍了故障诊断与隔离(FDI)的发展阶段和工作机理,研究了误警产生的原因及对策。

基于最小割集排序的航天器故障定位方法

针对故障树分析结果，根据Ｆｕｓｓｅｌ－Ｖｅｓｅｌｙ重要度值在０．５附近的基本事件优先考察的原则，区分最小割集，并对它们进行失效影响概率排序．应用该方法可有效地反映各最小割集引起顶事件发生的可能性大小，达到指导故障隔离、修复，提高诊断速度和诊断效率的目的．