Multisensor switching control strategy with fault tolerance guarantees

In this paper we propose a novel fault tolerant multisensor switching strategy for feedback control. Each sensor of the proposed multisensor scheme has an associated state estimator which, together with a state feedback gain, is able to individually stabilise the closed-loop system. At each instant of time, the switching strategy selects the sensor-estimator pair that provides the best closed-loop performance, as measured by a control-performance criterion. We establish closed-loop stability of the resulting switching scheme under normal (fault-free) operating conditions. More importantly, we show that closed-loop stability is preserved in the presence of faulty sensors if a set of conditions on the system parameters (such as bounds on the sensor noises, maximum and minimum values of the reference signal, etc.) is satisfied. This result enhances and broadens the applicability of the proposed multisensor scheme since it provides guaranteed properties such as fault tolerance and robust closed-loop stability under sensor fault. The results are applied to the problem of automotive longitudinal control.     

基于故障树分析的雷位误差可靠性研究

根据鱼雷雷位误差影响因素,构建雷位误差试验失败为顶事件的故障树,进行雷位误差可靠性的定性分析和定量分析,根据重要度分析计算的关键重要度,得出了各因素对雷位误差系统的影响程度,确定了系统的薄弱环节以及维修故障的顺序。通过控制这些关键部件的可靠度便能提高系统的可靠度,从而提高鱼雷雷位误差试验的可靠度。     

Reliability growth modeling and optimal release policy under fuzzy environment of an N-version programming system incorporating the effect of fault removal efficiency

Failure of a safety critical system can lead to big losses.Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems.Fault-tolerant softwares are used to increase the overall reliability of software systems.Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery (recovery block scheme),fault masking (N-version programming (NVP)) or a combination of both (Hybrid scheme).These softwares incorporate the ability of system survival even on a failure.Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems.Most of them consider the stable system reliability.Few attempts have been made in reliability modeling to study the reliability growth for an NVP system.Recently,a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency.In this model,a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed.In this paper,we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation.Using this model,a software reliability growth model (SRGM) is developed to model the reliability growth of an NVP system.The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required.It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost.In this paper,we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.     

An adaptive scheme for fault-tolerant scheduling of soft real-time tasks in multiprocessor systems

The scheduling of real-time tasks with primary-backup-based fault-tolerant requirements has been an important problem for several years. Most of the known scheduling schemes are non-adaptive in nature meaning that they do not adapt to the dynamics of faults and task's parameters in the system. In this paper, we propose an adaptive fault-tolerant scheduling scheme that has a mechanism to control the overlap interval between the primary and backup versions of tasks such that the overall performance of the system is improved. The overlap interval is determined based on the observed fault rate and task's soft laxity. We also propose a new performance index, called SR index, that integrates schedulability (S) and reliability (R) into a single metric. To evaluate the proposed scheme, we have conducted analytical and simulation studies under different fault and deadline scenarios, and found that the proposed adaptive scheme adapts to system dynamics and offers better SR index than that of the non-adaptive schemes.     

Conditions for detecting a logical element fault in a combination device under concurrent checking based on Berger’s code

We formulate necessary and sufficient conditions for detecting a fault at the output of a logical element in a combination logical device in a concurrent error-detection system based on Berger’s code. We introduce the notion of a fully testable fault. We show that in order to ensure that all single faults in combination devices are detected with their concurrent checking based on Berger’s code we can use not only the property that it detects 100% of unidirectional errors but also the property that it detects 100% of asymmetrical errors. Due to the latter property, we can reduce structural redundancy of the combination device, when transforming its circuit to one amenable for control, compared to known algorithms for modifying the structure of combination devices into circuits with unidirectionally independent outputs.     

The development of a fault-tolerant control approach and its implementation on a flexible arm robot

This article presents a robust sensor fault-tolerant control (FTC) scheme and its implementation on a flexible arm robot. Sensor faults affect the system's performance in the closed loop when the faulty sensor readings are used to generate the control input. In this article, the non-faulty sensors are used to reconstruct the faults on the potentially faulty sensors. The reconstruction is subtracted from the faulty sensors to generate a 'virtual sensor' which (instead of the normally used faulty sensor output) is then used to generate the control input. A design method is also presented in which the virtual sensor is made insensitive to any system uncertainties (which could corrupt the fault reconstruction) that cannot fit into the framework of the model used. Two fault conditions are tested: total failure and incipient faults. Then the scheme robustness is tested and evaluated through its implementation on two flexible arm systems, one with a flexible joint and the other with a flexible link. Excellent results have been obtained for both cases (joint and link); the FTC scheme produced system performance almost identical to the fault-free scenario, whilst providing an indication that a fault is present, even for simultaneous faults.     

Fault‐tolerant fusion‐based MPC with sensor recovery for constrained LPV systems

In this paper, we present a robust fault‐tolerant control scheme for constrained multisensor linear parameter‐varying systems, subject to bounded disturbances, that utilises multiple sensor fusion. The closed‐loop scheme consists of a tube model predictive control‐based feedback tracking controller and sensor‐estimate fusion strategy, which allows for the reintegration of previously faulty sensors. The active fault‐tolerant fusion‐based mechanism tracks the healthy‐faulty transitions of suitable residual variables by means of set separation and precomputed transition times. The sensor‐estimate pairings are then reconfigured based on available healthy sensors. Under the proposed scheme, robust preservation of closed‐loop system boundedness is guaranteed for a wide range of sensor fault situations. An example is presented to illustrate the performance of the fault‐tolerant control strategy.     

基于FIR神经网络的风电场虚拟风速传感器

风电机组风速传感器易发故障,故障可能导致机组安全风险和发电量损失。针对现行的故障处理方法因与机组控制策略紧密耦合而日益面临挑战,提出了一种基于数据处理的虚拟风速传感器原理与方法：由风电场上风向测量风速计算下风向推算风速,用推算风速取代故障传感器。着重讨论了基于FIR神经网络的推算风速计算方法和计算模型,探讨了系统实现的关键技术。实验证明了虚拟传感器的误差在机组控制系统可接受的程度内。提出的方法独立于机组自身属性,具有普遍适用性,可部署在任意类型的场,在物理传感器故障时向机组提供风速信号,支撑风电机组持续安全运行。     

Model predictive control-based fault detection and reconstruction algorithm for longitudinal control of autonomous driving vehicle using multi-sliding mode observer

This paper presents a model predictive control-based fault detection and reconstruction algorithm for longitudinal control of autonomous driving using a multi-sliding mode observer. In order to secure the safe longitudinal control of a vehicle, a numbers of factors must be ensured, such as the reliability of the longitudinal information, the data on the forward object from the environment sensor, and the acceleration of the ego vehicle. Thus, we propose a reasonable failure detection scheme for the acceleration signal of the host vehicle and the relative values of the front object of the radar. In order to identify the faults of the radar and the vehicle acceleration sensor related to the automated longitudinal control, the multiple sliding mode observer and prediction of model predictive control (MPC) algorithm are applied. The relative acceleration is reconstructed by applying a sliding mode observer (SMO) with clearance and relative speed measurements. The upper and lower limits of longitudinal acceleration were computed by analyzing human driving data under the preceding vehicle and reconstructed acceleration. A proper acceleration range can be defined precisely based on several reconstructed upper and lower bounds by using a multiple sliding mode observer with stored prediction data of relative values, making it possible to effectively identify the fault of the host vehicle’s acceleration sensor. By applying MPC for this study, optimal control input and prediction of relative states can be obtained that are more reasonable than those using the linear prediction model. The proposed fault detection algorithm can identify the abnormal state of the environment sensors by using the accumulated past sensor data. By comparing the stored prediction of relative states with the stored data on current states for a given period, the signal faults of the longitudinal target information can be detected from environment sensors. With these fault indices of states, the final fault diagnoses of sensors can be determined by assessing confidence through statistical analysis of 27 sets of normal driving data. In order to obtain a reasonable performance evaluation, this study uses actual driving data and a 3D full vehicle model constructed in the MATLAB/Simulink environment. The test results reveal that the proposed algorithm can successfully detect the fault of the radar and acceleration sensor of the automated driving vehicle.

     

Set theoretic approach to fault-tolerant control of linear parameter-varying systems with sensor reintegration

In this paper, we present a fault-tolerant control scheme for linear parameter-varying systems that utilises multiple sensor switching to compensate for sensor faults. The closed-loop scheme consists of an estimator-based feedback tracking controller and sensor-estimate switching strategy which allows for the reintegration of previously faulty sensors. The switching mechanism tracks the transitions from faulty to healthy behaviour by means of set separation and pre-computed transition times. The sensor-estimate pairings are then reconfigured based on available healthy sensors. Under the proposed scheme, preservation of closed-loop system boundedness is guaranteed for a wide range of sensor fault situations. An example is presented to illustrate the performance of the fault-tolerant control strategy.     

基于故障描述因子与多观测器同步协作的AUV执行机构故障隔离与辨识

执行机构的可靠性对系统的安全运行具有重要意义.针对自主式水下机器人(AUV),提出一种基于故障描述因子与多观测器同步协作的故障隔离与辨识方法.首先,通过执行机构故障描述因子概念的引入,将AUV运载器的故障隔离和辨识问题分为两个层级;其次,通过扩张状态观测器(ESO)和故障隔离逻辑规则的设计,实现包括舵面、螺旋桨与舵机在内的粗粒度级执行机构的故障隔离;然后,根据故障力与力矩的正负性关系,进一步分析舵面的具体形变故障类型,亦或基于螺旋桨与舵机的输出力与力矩关系,通过分析故障因子的内部信息,具体辨识可能的螺旋桨与舵机故障类型;最后,通过仿真结果表明所提出方法对AUV执行机构的故障具有较好的隔离与辨识精度.     

基于ECC的无可信中心的组密钥生成协议

对无可信中心的可认证秘密共享协议进行分析,发现其不具有完全前向安全性,针对该问题,提出一种基于椭圆曲线密码体制与Shamir门限思想的组密钥生成协议,在该协议中成员可以对收到的信息进行验证,有效防止主动与被动攻击,同时系统也无需安全信道,降低系统代价。实验结果表明,该协议具有较高的安全性和计算效率。     

Robust parameter-dependent fault-tolerant control for actuator and sensor faults

In this article, we study a robust fault-tolerant control (FTC) problem for linear systems subject to time-varying actuator and sensor faults. The faults under consideration are loss of effectiveness in actuators and sensors. Based on the estimated faults from a fault detection and isolation scheme, robust parameter-dependent FTC will be designed to stabilise the faulty system under all possible fault scenarios. The synthesis condition of such an FTC control law will be formulated in terms of linear matrix inequalities (LMIs) and can be solved efficiently by semi-definite programming. The proposed FTC approach will be demonstrated on a simple faulty system with different fault levels and fault estimation error bounds.     

容错计算机联锁系统

容错计算机联锁系统是使用容错计算机构成的车站计算机联锁系统,其基本思想是：通过TMR的硬件冗余方式,实现系统的高可靠性和高安全性.该文将介绍我国两种典型的容错计算机联锁系统(TYJL&TR9、DS6&20型),并对其可靠性和安全性进行定量的分析.     

Diagnosis using a first-order stochastic language that learns

We have created a diagnostic/prognostic software tool for the analysis of complex systems, such as monitoring the “running health” of helicopter rotor systems. Although our software is not yet deployed for real-time in-flight diagnosis, we have successfully analyzed the data sets of actual helicopter rotor failures supplied to us by the US Navy. In this paper, we discuss both critical techniques supporting the design of our stochastic diagnostic system as well as issues related to its full deployment. We also present four examples of its use.Our diagnostic system, called DBAYES, is composed of a logic-based, first-order, and Turing-complete set of software tools for stochastic modeling. We use this language for modeling time-series data supplied by sensors on mechanical systems. The inference scheme for these software tools is based on a variant of Pearl’s loopy belief propagation algorithm [Pearl, P. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco, CA: Morgan Kaufmann]. Our language contains variables that can capture general classes of situations, events, and relationships. A Turing-complete language is able to reason about potentially infinite classes and situations, similar to the analysis of dynamic Bayesian networks. Since the inference algorithm is based on a variant of loopy belief propagation, the language includes expectation maximization type learning of parameters in the modeled domain. In this paper we briefly present the theoretical foundations for our first-order stochastic language and then demonstrate time-series modeling and learning in the context of fault diagnosis.     

Sensor fault‐tolerant control of a magnetic levitation system

In this paper, a fault‐tolerant switching control strategy is implemented on a magnetic levitation (MAGLEV) system. Two sensors are embedded in the MAGLEV system and their measurements used by two independent estimators. Each sensors–estimator combination, together with a feedback controller can levitate and stabilize a 1‐in steel ball at a desired position in the air. The paper focuses on the design and testing of a switching scheme which, at each instant of time, selects the sensors–estimator combination that provides the best closed loop performance based on a chosen criterion. Theoretical results on the system linearization around an operating point ensure local closed‐loop stability and good performance under the occurrence of an abrupt fault in one of the plant sensors. Experimental results are provided which confirm the fault‐tolerant capabilities of the strategy. Copyright © 2010 John Wiley & Sons, Ltd.     

A novel general approach to evaluating the PCBA for components with different membership function

The logistic support of high tech industry all emphasize that reliability design start, one must fully considers its life cycle. The reliability analysis and prediction are the main work objectives. The purpose is to ensure that the system (product) can achieve its designed functions under specific operating conditions. However, the incomplete failure data and different fault probability density function of the system elementary event also increase the difficulty of reliability design and calculation. It cannot be fully solved by traditional probability reliability. Therefore, a more general and efficient algorithm to assess the system reliability is needed. This paper proposed speculation by experts’ opinions according to incomplete information condition. It reasonably gives different fault membership function of possibility of failure distribution under different bottom event. It also applies fault-tree analysis, α-cut of vague set, and interval arithmetic operations of vague set to obtain fault interval and reliability interval of the system. Moreover, this paper also modifies Tanaka et al.’s fuzzy fault-tree definition and extended the new usage to fit different membership function of vague fault-tree. Then, find out the critical elementary event that affects the reliability of the system, which could be used for managerial decision-making, and as the bases to future system maintenance strategy. In numerical verification, a malfunction of printed circuit board assembly (PCBA) is presented as a numerical example. The result of the proposed method is compared with the listing approaches of reliability analysis methods.     

基于神经网络的二次测爆仪传感器故障诊断方法

在全面分析智能二次测爆仪中传感器主要故障的基础上,提出了基于L-M算法的神经网络诊断方案.通过模拟矿井中各种气体体积分数所占比例的仿真实验证明:基于L-M算法的神经网络故障诊断系统具有良好的故障监测与诊断能力.     

Synthesis of fixed-structure robust controllers using a constrained particle swarm optimizer with cyclic neighborhood topology

The purpose of this paper is to develop design scheme based on an easy-to-use meta-heuristic approach with superior reliability and validity for fixed-structure robust controllers, satisfying both multiple control specifications and system stability conditions. For this purpose, a particle swarm optimizer is first developed, which reduces the probability of premature convergence to local optima in the PSO (particle swarm optimization) by exploiting the particle’s local social learning based on the idea of cyclic-network topology. Next, it is shown how to obtain a fixed-structure robust controller with constraints on multiple H_∞ specifications and system stability based on the developed PSO technique incorporated with a simple constraint handling method. Finally, typical numerical examples are studied to show the applicability of the proposed methodology to the synthesis of fixed-structure robust controllers. These examples clearly verify that the developed design scheme gives a novel and powerful impetus with remarkable reliability to fixed-structure robust controller syntheses.     

Integrity-directed sequential state estimation: Assessing high reliability requirements via safe confidence intervals

This study deals with the problem of dynamic state estimation of continuous-time systems from discrete-time measurements in the context of high-integrity applications. The objective of integrity-directed estimation is to provide confidence intervals for the state with an extremely low risk of error. We suppose that the process noise can be modelled by Gaussian sequences, and that the measurement noise is Gaussian in the normal operating mode of the sensors. The evolution of the posterior probability distribution of the system’s state is deduced from recursive linear MMSE estimation. The estimation scheme presented here is equivalent to the Kalman filter, with the difference that the data is not processed directly, but collected in sets in preparation for an ulterior, slightly delayed, grouped processing. This strategy is particularly suitable for fault detection, because the estimator naturally takes into account the cross-correlations of close-in-time measurements and the decisions can be based on more data. Next, we introduce dynamic tools for detecting faults and sensor failures. A full Bayesian modelling of the sensors leads to the derivation of a dynamic multiple-model estimator performing the linear MMSE state estimation under various fault hypotheses. This estimator provides estimates of the posterior density function of the state, on which safe confidence intervals certifying very high-integrity levels can be fixed. In practice, the exponential growth of the complexity of the multiple-model estimator requires the simplification of the posterior mixture distributions. A new method for limiting the complexity of the posterior distributions in an integrity-oriented context is presented. Unlike the known mixture simplification strategies (GPB, IMM), the present method has the property to quantify and minimize the loss in integrity of the process of simplification of the distributions. Finally, the estimator is tested on a typical rail navigation problem.