基于粗集理论的雷达故障诊断方法

针对雷达故障诊断中数据量庞大和属性维数多的问题，提出了一种基于粗集理论的雷达故障诊断方法，构建了故障诊断模型，介绍了诊断算法和诊断过程，并用实例进行了验证。结果表明，该方法能够有效地提高故障诊断的可靠性，减小诊断的不确定性。     

A New Real-Time Reliability Prediction Method for Dynamic Systems Based on On-Line Fault Prediction

While a specific system is in use, its reliability will decrease gradually after the infant mortality period because of the components' degradation, or external attacks. Thus, reliability is a natural characteristic of a system's health, and can be used for condition monitoring & predictive maintenance. This paper introduces a new real-time reliability prediction method for dynamic systems which incorporates an on-line fault prediction algorithm. The factors that may reduce a system's reliability are modeled as an additive fault input to the system, and the fault is assumed to be varying linearly with time, approximately. The time-varying fault is roughly estimated based on a modified particle filtering algorithm at first. Then, as a time series, the fault estimate sequence is smoothed, and predicted by an exponential smoothing method. Mathematical analysis shows that the effects of the system, and measurement noises on the fault estimates are greatly reduced by exponential smoothing, which indicates that the comparatively high accuracy of the fault estimates & predictions is guaranteed. Based on the particle filtering & fault prediction results, the whole system's predictive reliability is computed through a Monte Carlo simulation strategy. The effectiveness of the proposed real-time reliability prediction method is validated by a computer simulation of a three-vessel water tank system.     

Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly

Fault-tree analysis (FTA) is a powerful technique used to identify the root causes of undesired event in system failure by constructing a tree of sub-events, spreading into bottom events, procreating the fault and finally heading to the top event. From integrating expert’s knowledge and experience in terms of providing the possibilities of failure of bottom events, an algorithm of the intuitionistic fuzzy fault-tree analysis is proposed in this paper to calculate fault interval of system components and to find the most critical system component for the managerial decision-making based on some basic definitions. The proposed method is applied for the failure analysis problem of printed circuit board assembly (PCBA) to generate the PCBA fault-tree, fault-tree nodes, then directly compute the intuitionistic fuzzy fault-tree interval, traditional reliability, and the intuitionistic fuzzy reliability interval. The result of this proposed method is compared with the existing approaches of fault-tree methods.     

FPGA-based Monte Carlo simulation for fault tree analysis

The reliability analysis of critical systems is often performed using fault-tree analysis. Fault trees are analyzed using analytic approaches or Monte Carlo simulation. The usage of the analytic approaches is limited in few models and certain kinds of distributions. In contrast to the analytic approaches, Monte Carlo simulation can be broadly used. However, Monte Carlo simulation is time-consuming because of the intensive computations. This is because an extremely large number of simulated samples may be needed to estimate the reliability parameters at a high level of confidence.In this paper, a tree model, called Time-to-Failure tree, has been presented, which can be used to accelerate the Monte Carlo simulation of fault trees. The time-to-failure tree of a system shows the relationship between the time to failure of the system and the times to failures of its components. Static and dynamic fault trees can be easily transformed into time-to-failure trees. Each time-to-failure tree can be implemented as a pipelined digital circuit, which can be synthesized to a field programmable gate array (FPGA). In this way, Monte Carlo simulation can be significantly accelerated. The performance analysis of the method shows that the speed-up grows with the size of the fault trees. Experimental results for some benchmark fault trees show that this method can be about 471 times faster than software-based Monte Carlo simulation.     

System optimization with component reliability estimation uncertainty: a multi-criteria approach

Summary & Conclusions-This paper addresses system reliability optimization when component reliability estimates are treated as random variables with estimation uncertainty. System reliability optimization algorithms generally assume that component reliability values are known exactly, i.e., they are deterministic. In practice, that is rarely the case. For risk-averse system design, the estimation uncertainty, propagated from the component estimates, may result in unacceptable estimation uncertainty at the system-level. The system design problem is thus formulated with multiple objectives: (1) to maximize the system reliability estimate, and (2) to minimize its associated variance. This formulation of the reliability optimization is new, and the resulting solutions offer a unique perspective on system design. Once formulated in this manner, standard multiple objective concepts, including Pareto optimality, were used to determine solutions. Pareto optimality is an attractive alternative for this type of problem. It provides decision-makers the flexibility to choose the best-compromise solution. Pareto optimal solutions were found by solving a series of weighted objective problems with incrementally varied weights. Several sample systems are solved to demonstrate the approach presented in this paper. The first example is a hypothetical series-parallel system, and the second example is the fault tolerant distributed system architecture for a voice recognition system. The results indicate that significantly different designs are obtained when the formulation incorporates estimation uncertainty. If decision-makers are risk averse, and wish to consider estimation uncertainty, previously available methodologies are likely to be inadequate.     

Confidence bounds on respiratory mechanical properties estimatedfrom transfer versus input impedance in humans versus dogs

Using parameters typical of a dog, we have shown that estimates for the parameters in the six-element model of Dubois et al. would be very unreliable if either input (Z(in)) or transfer (Ztr) data from only 2-32 Hz were fit. It has subsequently been shown that this model is not appropriate for human Z(in) from 2-320 Hz. However, several studies have continued to apply the model to human Ztr data from only 2-32 Hz. In this study a sensitivity analysis is used to determine whether and why the six-element model could be applicable to lower frequency (less than 64 Hz) Ztr data in humans, but not Z(in) data over any frequency range. We first predicted the joint parameter uncertainty bounds assuming a fit to either 2-32 Hz Z(in) or Ztr data created from literature based mean parameter values. Consistent with previous studies, we predicted that the estimates will be very unreliable if obtained from Z(in) data for humans or dogs, or from Ztr data from dogs. Surprisingly, however, the reliability of several parameter estimates from human Ztr data from only 2-32 Hz are reasonable. We next evaluated the variability in 2-64 Hz based Ztr parameter estimates by comparing experimental variability in two healthy human subjects (over 10 and 13 trials) to theoretical and Monte Carlo numerical predictions based on a single trial. Again, the Ztr parameters were reliable. A simulation study was used to describe the reasons for enhanced reliability when using human Ztr data. It is shown that this reliability is largely dependent on alveolar gas compressibility, Cg.(ABSTRACT TRUNCATED AT 250 WORDS)     

On improved confidence bounds for system reliability

In this paper, new bounding strategies are presented to improve confidence interval estimation for system reliability based on component level reliability, and associated uncertainty data. Research efforts have been focused on two interdependent areas: 1) development & improvement of analytical approaches for quantifying the uncertainty associated with the system reliability estimate when data regarding component reliability is available; and 2) based on these analytical approaches, generating statistical inference methods that can be used to make accurate estimations about the reliability of a system. The analytical approach presented relies on a recursive rationale that can be applied to obtain the variance associated with the system reliability estimate, provided the system can be decomposed into a series-parallel configuration. The bounding procedure is independent of parametric assumptions regarding component time to failure, and can be applied whenever component reliability data are available. To assess the validity of the proposed procedure, three test cases have been analyzed. For each case, Monte-Carlo simulation has been used to generate component failure data, based on nominal component reliability values. Based on these simulated data, lower bounds have been constructed, and then compared against nominal system reliability to generate an expected confidence level. The results obtained exhibit a significant improvement in the accuracy of the confidence intervals for the system reliability when compared with existing approximation methods. The procedure described is effective, relatively simple, and widely applicable.     

Development of a neural network based algorithm for rainfallestimation from radar observations

Rainfall estimation based on radar measurements has been an important topic in radar meteorology for more than four decades. This research problem has been addressed using two approaches, namely a) parametric estimates using reflectivity-rainfall relation (Z-R relation) or equations using multiparameter radar measurements such as reflectivity, differential reflectivity, and specific propagation phase, and b) relations obtained by matching probability distribution functions of radar based estimates and ground observations of rainfall. In this paper the authors introduce a neural network based approach to address this problem by taking into account the three-dimensional (3D) structure of precipitation. A three-layer perceptron neural network is developed for rainfall estimation from radar measurements. The neural network is trained using the radar measurements as the input and the ground raingage measurements as the target output. The neural network based estimates are evaluated using data collected during the Convection and Precipitation Electrification (CaPE) experiment conducted over central Florida in 1991. The results of the evaluation show that the neural network can be successfully applied to obtain rainfall estimates on the ground based on radar observations. The rainfall estimates obtained from neural network are shown to be better than those obtained from several existing techniques. The neural network based rainfall estimate offers an alternate approach to the rainfall estimation problem, and it can be implemented easily in operational weather radar systems     

A nonparametric nonstationary procedure for failure prediction

The time between failures is a very useful measurement to analyze reliability models for time-dependent systems. In many cases, the failure-generation process is assumed to be stationary, even though the process changes its statistics as time elapses. This paper presents a new estimation procedure for the probabilities of failures; it is based on estimating time-between-failures. The main characteristics of this procedure are that no probability distribution function is assumed for the failure process, and that the failure process is not assumed to be stationary. The model classifies the failures in Q different types, and estimates the probability of each type of failure s-independently from the others. This method does not use histogram techniques to estimate the probabilities of occurrence of each failure-type; rather it estimates the probabilities directly from the values of the time-instants at which the failures occur. The method assumes quasistationarity only in the interval of time between the last 2 occurrences of the same failure-type. An inherent characteristic of this method is that it assigns different sizes for the time-windows used to estimate the probabilities of each failure-type. For the failure-types with low probability, the estimator uses wide windows, while for those with high probability the estimator uses narrow windows. As an example, the model is applied to software reliability data.     

A weighted-principal component regression method for the identification of physiologic systems

We introduce a system identification method based on weighted-principal component regression (WPCR). This approach aims to identify the dynamics in a linear time-invariant (LTI) model which may represent a resting physiologic system. It tackles the time-domain system identification problem by considering, asymptotically, frequency information inherent in the given data. By including in the model only dominant frequency components of the input signal(s), this method enables construction of candidate models that are specific to the data and facilitates a reduction in parameter estimation error when the signals are colored (as are most physiologic signals). Additionally, this method allows incorporation of preknowledge about the system through a weighting scheme. We present the method in the context of single-input and multi-input single-output systems operating in open-loop and closed-loop. In each scenario, we compare the WPCR method with conventional approaches and approaches that also build data-specific candidate models. Through both simulated and experimental data, we show that the WPCR method enables more accurate identification of the system impulse response function than the other methods when the input signal(s) is colored.     

Interval Reliability for Initiating and Enabling Events

This paper describes generation and evaluation of logic models such as fault trees for interval reliability. Interval reliability assesses the ability of a system to operate over a specific time interval without failure. The analysis requires that the sequence of events leading to system failure be identified. Two types of events are described: 1) initiating events (cause disturbances or perturbations in system variables) that cause system failure and 2) enabling events (permit initiating events to cause system failure). Control-system failures are treated. The engineering and mathematical concepts are described in terms of a simplified example of a pressure-tank system. Later these same concepts are used in an actual industrial application in which an existing chlorine vaporizer system was modified to improve safety without compromising system availability. Computer codes that are capable of performing the calculations, and pitfalls in computing accident frequency in fault tree analysis, are discussed.     

A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network

With the wide applications of wireless sensor network (WSN), its reliability evaluation has been attracted more attention. The reliability of a WSN is affected mainly by internal and external factors, which include internal faults and external attacks. In this paper, a reliability evaluation method based on a hierarchical belief rule base (BRB) method is proposed for the reliability evaluation of the WSN. First, the factors affecting the reliability of a WSN are analysed, and the reliability evaluation process that considers the WSN fault evaluation and WSN security evaluation is described. Second, the reliability evaluation model is constructed based on the hierarchical BRB model. The qualitative knowledge is used by the BRB model to build initial belief rules, and the quantitative data are used to optimize the initial parameters of the BRB model, which can utilize various types of uncertainty information effectively. Therefore, the proposed method can be applied to the WSN reliability evaluation, which is a complex and uncertain problem. Finally, a simulation case study and an actual case study of wellhead blowout monitoring are conducted to verify the effectiveness of the proposed method. The reliability results of actual WSN are obtained by the standard testing method, where the loss and accuracy rates of the collected data are treated as the observation factors for obtaining the actual reliability values. The estimated results of hierarchical BRB model are very close to the actual reliability values, which shows that the proposed method can be used for evaluating the reliability of the actual WSN accurately.     

Path-Based Error Coverage Prediction

We present an analytical technique that uses fault injection data for estimating the coverage of concurrent error detection mechanisms in microprocessors. A major problem in such estimations is that the coverage depends on the program executed by the microprocessor as well as the input sequence to the program. We propose a method that predicts the error coverage for a specified input sequence based on fault injection data obtained for another input sequence. Our results show that post-injection analysis is a promising approach for reducing the cost of coverage estimation.     

故障模糊诊断方法及其应用

现代系统一般都采用模块化设计,功能层次比较明显,利用这种功能特点可加速系统的故障诊断。据此,综合层次法和模糊识别法,本文给出了一种基于层次分析和模糊识别的故障诊断方法。该方法根据系统功能特点划分故障模块,使故障具有一定的层次结构,从而加速了故障的定位;再结合模糊识别法进行故障定位,有效地解决了故障状态识别的不确定性问题。最后结合实例分析,验证了此方法的有效性,并具有故障诊断快速、准确的特点。     

基于模块化思想的复杂故障树重要度算法研究

如何高效地计算大规模故障树的重要度是当前可靠性工程领域的难点之一。介绍了计算复杂故障树重要度的基本原理,针对当前重要度算法存在复杂度高、效率低下等问题,在应用模块化思想进行故障树分析的基础上,考虑共因事件对计算规模的影响,实现了一种更高效的重要度算法,并验证了该算法的正确性与有效性。     

Robust ℋ∞ filtering for continuous timevarying uncertain systems with deterministic input signals

Many dynamical systems involve not only process and measurement noise signals but also parameter uncertainty and known input signals. When ℒ₂ or ℋ_∞ filters that were designed based on a “nominal” model of the system are applied, the presence of parameter uncertainty will not only affect the noise attenuation property of the filter but also introduce a bias proportional to the known input signal, and the latter may be very appreciable. We introduce a finite-horizon robust ℋ_∞ filtering method that provides a guaranteed ℋ_∞ bound for the estimation error in the presence of both parameter uncertainty and a known input signal. This method is developed by using a game-theoretic approach, and the results generalize those obtained for cases without parameter uncertainty or without a known input signal. It is also demonstrated, via an example, that the proposed method provides significantly improved signal estimates     

Probabilistic analysis of dynamic and temporal fault trees using accurate stochastic logic gates

This paper presents accurate models for the analysis of fault trees based on stochastic logic. To produce the models, probabilistic analysis of static, dynamic and temporal gates is carried out and the probability models are converted to their equivalent stochastic logic gates. A hardware template is also designed for each stochastic logic gate. In the proposed method, users provide fault rates of basic events and immediately evaluate system reliability. Experimental results show that the proposed method is more accurate than previous methods using the proposed stochastic logic gates for dynamic and temporal fault trees. The formula was validated using the Markov model for exponential failure distribution events. The proposed model is applicable for both exponential and non-exponential distributions.     

考虑时间因素的不同基本门故障概率计算

在门级电路的可靠性概率评估方法中,基本门的故障概率p一般人为设定或以常数形式出现.考虑到不同基本门的故障概率具有随时间变化的特性并结合其输入导线,本文构建了考虑输入负载的随时间变化的不同基本门的故障概率模型.理论分析与实验结果表明,基于弱链接模型的双峰对数正态分布更适合用来表示输入导线故障概率的时间分布.用本文方法、美国军用标准MIK-HDBK-217及Monte Carlo方法计算了ISCAS85基准电路的可靠度并进行了比较,还通过了行业标准的检验,结果验证了本文所构建模型的合理性.     

Method of reliability analysis of control systems for nuclear power plants

High reliability is one of the main objectives of the design and operation of Control Systems in Nuclear Power Plants. This paper presents a method of reliability analysis for these systems using various reliability techniques and engineering judgement. The step-by-step analysis includes system study, field data, failure mode and effect analysis, common mode failures, fault trees, human factors, reliability targets, and design reviews. To illustrate this method, the Liquid Zone Control System for CANDU nuclear reactor control is used.     

Reduced State Enumeration?Another Algorithm for Reliability Evaluation

This new algorithm for reliability evaluation is a refinement of the state enumeration method. The set of enumerated states is different for different parts of the fault tree, in particular the number of enumerated states does not necessarily increase towards the top event. This method makes it feasible to treat some large fault trees analytically rather than by Monte Carlo simulation. A lot of research remains to be done, like programming and evaluating the algorithm, and finding intrinsic measures of difficulty for fault trees.