An output delay approach to fault estimation for sampled-data systems

The problem of fault estimation for a class of non-uniformly sampled-data systems is investigated from the time delay point of view in this paper.Firstly,the output delay approach is employed to model the sampled-data system as a continuous-time one with time-varying delay output.Then,based on the analysis of the inapplicability of the adaptive fault diagnosis observer in such class of time-delay systems,a novel augmented fault estimation observer design method is proposed to guarantee the exponential convergence of the estimation errors.Furthermore,an extension to the case of time varying fault estimation for the noisy sampled-data systems is studied.Finally,simulation results of a flight control system are presented to demonstrate the effectiveness of the proposed method.     

Fuzzy reliability estimation using Bayesian approach

The Bayesian reliability estimation under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed to be fuzzy random variables with fuzzy prior distributions. The (conventional) Bayesian estimation method will be used to create the fuzzy Bayes point estimator of reliability by invoking the well-known theorem called ‘Resolution Identity’ in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.     

An approach for analyzing the reliability of industrial systems using soft-computing based technique

The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system by utilizing vague, imprecise, and uncertain data. In this, two important tools namely traditional Lambda–Tau and artificial bee colony algorithm have been used to build a technique named as an artificial bee colony (ABC) algorithm based Lambda–Tau (ABCBLT). In real-life situation, data collected from various resources contains a large amount of uncertainties due to human errors and hence it is not easy to analyze the behavior of such system up to a desired accuracy. If somehow behavior of these systems has been calculated, then they have a high range of uncertainty. For handling this situation, a fuzzy set theory has been used in the analysis and an artificial bee colony has been used for determining their corresponding membership functions. To strengthen the analysis, various reliability parameters, which affects the system performance directly, have been computed in the form of fuzzy membership functions. Sensitivity as well as performance analysis has also been analyzed and their computed results are compared with the existing techniques result. The butter–oil processing plant, a complex repairable industrial system has been taken to demonstrate the approach.     

Compromise: An effective approach for the hierarchical design of structural systems

In this paper the use of the compromise decision support problem in hierarchical design of structural systems is described. In the past we had postulated the hierarchical decision support problem for use in hierarchical design. In this paper, for the first time, the mathematical template that supports the underlying precepts of hierarchical design in the context of the Decision Support Problem Technique is presented. A structural example that demonstrates the efficacy of the approach is included.     

Parity Relation Based Fault Estimation for Nonlinear Systems： An LMI Approach

This paper proposes a parity relation based fault estimation for a class of nonlinear systems which can be modelled by Takagi-Sugeno （TS） fuzzy models. The design of a parity relation based residual generator is formulated in terms of a family of linear matrix inequalities （LMIs）. A numerical example is provided to illustrate the effectiveness of the proposed design techniques.     

An LMI approach to robust fault estimation for a class of nonlinear systems

The paper presents a robust fault estimation approach for a class of nonlinear discrete‐time systems. In particular, two sources of uncertainty are present in the considered class of systems, that is, an unknown input and an exogenous external disturbance. Thus, apart from simultaneous state and fault estimation, the objective is to decouple the effect of an unknown input while minimizing the influence of the exogenous external disturbance within the framework. The resulting design procedure guarantees that a prescribed disturbance attenuation level is achieved with respect to the state and fault estimation error while assuring the convergence of the observer. The core advantage of the proposed approach is its simplicity by reducing the fault estimation problem to matrix inequalities formulation. In addition, the design conditions ensure the convergence of the observer with guaranteed performance. The effectiveness of the proposed approach is demonstrated by its application to a twin rotor multiple‐input multiple‐output system. Copyright © 2015 John Wiley & Sons, Ltd.     

复杂系统可靠性估计的模糊神经Petri网方法

针对复杂系统可靠性建模难问题,提出了一种新的适用于复杂系统可靠性估计的模糊神经Petri网(简称为FNPN)．文中首先给出了模糊神经Petri网的定义及其引发规则,然后给出了一种学习算法．该FNPN结合了模糊Petri网和神经网络各自的优点,既可以表示和处理模糊产生式规则的知识库系统又具有学习能力,可通过对样本数据学习调整模型中的参数以获得系统内部的等效结构,从而计算出非样本数据的系统的可靠度．最后以一无向网络为例说明该方法是可行的．     

Total variance approach to software reliability estimation

This paper presents a mathematical model for a new approach to calculating the confidence intervals for software reliability projections. Unlike those calculated by current methods, these confidence intervals account for any uncertainty concerning the operational profile of the system     

An alternative approach to state estimation in linear time-invariant systems

An alternative approach to state estimation problem in linear, time-invariant dynamic systems is presented in this paper. The approach developed first identifies the initial state of the system by using a proportional plus integral parameter identification method. The Lyapunov design technique is used to guarantee the asymptotic convergence of the initial state identifier. A state estimator is then constructed to operate in series with the initial state identifier. The estimator generates an estimate of the unobserved part of the system state. Simulation studies have shown that satisfactory state estimation can be achieved in the presence of measurement or disturbance noise. An example problem is considered to demonstrate the response characteristics of the estimator-identifier combination.     

Compromise: An effective approach for the design of damage tolerant structural systems

In this paper, a general multiple objective formulation of the Compromise Decision Support Problem is applied to the design of damage tolerant structural systems. Damage tolerant design of structural systems is discussed in general and a design methodology is developed. A structure designed with damage tolerant considerations will have the capability to resist several levels of failure. To support the design methodology, a mathematical template for the design of a damage tolerant structural system is detailed. The basis for the template is the compromise decision support problem. Finally, the effectiveness of the formulation is demonstrated through the solution of a 13-bar truss. The results of the solutions are examined and insights are noted and discussed. The emphasis, however, is on the method and its capabilities and not on the utility of the final 13-bar truss design.     

An uncertainty-based approach to discrete-time fault estimation observer design for nonuniformly sampled systems

This paper considers the fault estimation problem of nonuniformly sampled system in which sensor sampling is performed at aperiodic interval. After being discretized at sampling instant, the nonuniformly sampled system is modeled as an equivalent polytopic system with norm bounded uncertainties. A discrete-time time-varying fault estimation observer with multiple design freedom is then constructed, and a sufficient condition given in linear matrix inequality (LMI) is provided to obtain the constant filter gain and ensure not only the asymptotic stability of fault estimation error but also the robustness of uncertainties. Compared with the existing observer designed based on continuous-time delay approach, the proposed one has a better estimation accuracy and less conservatism and is easy for digital implementation. A numerical simulation and a quadruple-tank benchmark are used to demonstrate the effectiveness and superiority of the proposed method.     

An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems

Frontiers of Information Technology & Electronic Engineering - A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code...     

A two-stage Subset Simulation-based approach for calculating the reliability of inelastic structural systems subjected to Gaussian random excitations

This paper presents a methodology for calculating the reliability of inelastic structural systems subjected to Gaussian random excitations. The method adopts a two-stage approach, involving separate calculation of the failure probabilities associated with linear elastic and inelastic structural response. The method exploits the fact that the calculation of failure probabilities associated with a linear problem can be performed extremely efficiently, using minimal computational effort compared to the effort required for solving a corresponding nonlinear problem. The calculation of failure probability associated with inelastic response is performed using a modified Subset Simulation procedure where the first step involves the direct simulation of samples in the inelastic domain rather than standard Monte Carlo simulations as in Standard Subset Simulation. It is demonstrated with a numerical example that the proposed two-stage approach offers significant computational savings over the Standard Subset Simulation approach.     

An enhanced approach to improve the encryption of big data using intelligent classification technique

Multimedia Tools and Applications - Data is undoubtedly one of the most significant assets in the current competitive era and to ensure its value is retained, data safety emerges as a principle...     

基于状态验证覆盖的Bayes软件可靠性评估

传统的可靠性评估方法都是基于系统软件运行期间的失效,对于武器系统软件,由于其使用试验耗费巨资且周期很长,不可能对系统进行过多的使用试验,导致难以采集到高质量的失效数据。提出一种基于系统状态验证覆盖的Bayes软件可靠性评估方法,该方法以Bayes可靠性模型为评估准则,通过状态覆盖率来保证充分性,通过状态测试验证来保证可靠性,提倡可信性与可靠性并行增长。     

An optimal approach for random signals classification

A method is proposed which solves the problem of the Bayes classification of ARMA (autoregressive moving average) signals when the models of classes and samples are not exactly known but only estimated from finite-length data sequences. Justified approximations and the hypothesis lead to decision rules including the variances of the estimations. The results obtained on a large set of simulated data show that this approach is superior to the best classical methods (cepstral distance or Kullback divergence), particularly in the common case where the hypothesis of those methods is not verified (short samples. small training sets. random classes)     

Model-driven estimation approach for system reliability using integrated tasks and resources

The increasing complexity of software systems in embedded systems or industrial business domains has led to the importance of reliability analysis for current systems. Reliability analysis has become a crucial part of the system development life cycle, and a new approach is needed to enable an early analysis for reliability estimation, especially for the system under design. However, the existing approach neglects the correlation between system resource and system task for estimating system reliability. This subsequently restricts accuracy of the estimation as well as causing difficulties in identifying critical resources and tasks during the design phase. This paper proposes a model-driven system reliability estimation using a scenario-based approach to estimate system reliability and identify its critical resources and system tasks during the design phase. This model is based on the PerFAM model, which can specifically view timing failures through a system scenario. The proposed approach is validated by the application of a sensitivity analysis into one case study. The case study demonstrates an essential relationship between system reliability, as well as both resources and tasks, which ultimately becomes the integral part for a system reliability estimation assessment.     

A response surface approach for structural reliability analysis using evidence theory

Evidence theory employs a much more general and flexible framework to quantify the epistemic uncertainty, and thereby it is adopted to conduct reliability analysis for engineering structures recently. However, the large computational cost caused by its discrete property significantly influences the practicability of evidence theory. This paper proposes an efficient response surface (RS) method to evaluate the reliability for structures using evidence theory, and hence improves its applicability in engineering problems. A new design of experiments technique is developed, whose key issue is the search of the important control points. These points are the intersections of the limit-state surface and the uncertainty domain, thus they have a significant contribution to the accuracy of the subsequent established RS. Based on them, a high precise radial basis functions RS to the actual limit-state surface is established. With the RS, the reliability interval can be efficiently computed for the structure. Four numerical examples are investigated to demonstrate the effectiveness of the proposed method.