An interval-valued hesitant fuzzy multigranulation rough set over two universes model for steam turbine fault diagnosis

In the field of mechanical engineering, steam turbine fault diagnosis is a difficult task for mechanical engineers who are confronted with challenges in dealing with copious amounts of uncertain information. Different mechanical engineers may have their own opinions about the system fault knowledge base that differs slightly from other mechanical engineers. Thus, to solve the problems presented by uncertain data analysis and group decision-making in steam turbine fault diagnosis, we propose a new rough set model that combines interval-valued hesitant fuzzy sets with multigranulation rough sets over two universes, called an interval-valued hesitant fuzzy multigranulation rough set over two universes. In the multigranulation framework, both basic definitions and some important properties of the proposed model are presented. Then, we develop a general approach to steam turbine fault diagnosis by using the proposed model. Lastly, an illustrative example is provided to verify the established approach and demonstrate its validity and applicability.     

A NEW TRUST-REGION ALGORITHM FOR FINITE MINIMAX PROBLEM

In this paper, a new trust region algorithm for minimax optimization problems is proposed, which solves only one quadratic subproblem based on a new approximation model at each iteration. The approach is different from the traditional algorithms that usually require to solve two quadratic subproblems. Moreover, to avoid Maratos effect, the nonmonotone strategy is employed. The analysis shows that, under standard conditions, the algorithm has global and superlinear convergence. Preliminary numerical experiments are conducted to show the efficiency of the new method.     

Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets

Fault detection of rotating machinery by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in rotating machinery involve significant nonlinear dynamical properties. As a novel method, phase space reconstruction is used to study the effect of faults on the chaotic behavior, for the first time. Strange attractors in reconstructed phase space proof the existence of chaotic behavior. To quantify the chaotic vibration for fault diagnosis, a set of new features are extracted. These features include the largest Lyapunov exponent; approximate entropy and correlation dimension which acquire more fault characteristic information. The variations of these features for different healthy/faulty conditions are very good for fault diagnosis and identification. For the first time, a new chaotic feature space is introduced for fault detection, which is made from chaotic behavior features. In this space, different conditions of rotating machinery are separated very well. To obtain more generalized results, the features are introduced into a neural network to identify different faults in rotating machinery. The effectiveness of the new features based on chaotic vibrations is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.     

Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (eg, linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specified covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons.     

基于二次优化BP神经网络的期货价格预测

针对BP算法存在的不足,结合神经网络、遗传算法和主成分分析的优点,提出基于二次优化BP神经网络的期货价格预测算法.初次优化采用主成分分析法对网络结构进行优化,第二次优化采用自适应遗传算法对网络参数进行优化,将经过二次优化后建立的BP神经网络模型用于期货价格预测.经仿真检验,用新方法建立的模型对期货价格进行预测,在预测的精度和速度方面都优于单纯BP神经网络模型.     

Detection of incipient gear failures using statistical techniques

Gears are important components in most power transmission mechanisms.Failures of gears can cause heavy losses in industry. Conditionmonitoring and fault diagnosis of gears is therefore importantto improve safety and reliability of gearbox operations andreduce losses caused by gear failures. This research proposesa new diagnostic approach based on the statistical analysisof data. It investigates the use of Principal Components Analysis(PCA) to detect growing local faults in a two-stage industrialhelical gearbox. In this research, the vibration signal is usedto monitor fault conditions and a broken tooth is simulatedas a local fault. Since the early detection of faults is a challenge,small fault conditions were tested first as well as severe faultconditions. In order to examine the ability of the PCA to detectfault conditions, first the PCA-based model was created fornormal operating conditions. Any unexpected event such as afault condition causes a significant deviation from the PCAmodel, which is obtained from the normal condition data of thegearbox. The Square Prediction Error (SPE) was calculated todetect the fault conditions. When the vibration signal fromthe gearbox is representative of normal operation, the valueof the SPE shows very little fluctuation and remains under acertain threshold value. However, in the presence of the faultthe SPE fluctuates considerably beyond the threshold value.It is shown that the PCA-based statistical approach cannot onlybe used to detect severe fault conditions, but that it alsoreveals small growing fault conditions at very early stage.The technique also provides information about the state of thefault such as the location of the fault as well as its severity. Received 5 March 2001. Revised 12 December 2001. Accepted 17 January 2002.     

A model-hybrid approach for unconstrained optimization problems

In this paper, we propose a model-hybrid approach for nonlinear optimization that employs both trust region method and quasi-Newton method, which can avoid possibly resolve the trust region subproblem if the trial step is not acceptable. In particular, unlike the traditional trust region methods, the new approach does not use a single approximate model from beginning to the end, but instead employs quadratic model or conic model at every iteration adaptively. We show that the new algorithm preserves the strong convergence properties of trust region methods. Numerical results are also presented.     

单输入多时滞离散系统的线性二次调节问题

研究了单输入多时滞的离散时间系统的线性二次调节问题(LQR问题),给出了求解最优控制输入序列的一种简单有效而又新颖的方法.将该动态的离散时滞系统的LQR最优控制问题最终转化成了一个静态的、不带时滞的数学规划模型——带等式线性约束的严格凸二次规划问题,并利用两种方法解这个二次规划问题,均成功地导出了系统的最优控制输入序列.仿真结果验证了我们的方法的正确有效性.     

Modeling,analysis and performance evaluation for fault diagnosis and Fault Tolerant Control in bottle-filling plant modeled using Hybrid Petri nets

In this paper, an approach to achieve fault diagnosis and Fault Tolerant Control in a typical bottle-filling plant using event based techniques is discussed. For this purpose, the plant is modeled using Hybrid Petri nets which enable study and analysis with regard to the working of the plant. Once effective modeling is done based on two different case studies considered, new algorithms are proposed to achieve fault diagnosis and Fault Tolerant Control on the models developed. Finally, performance measures with regard to the models proposed are evaluated to check the correctness of the models developed. Both analytical and numerical results are obtained which are highly useful to understand plant behavior.     

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本文综述混合效应模型参数估计方面的若干新进展. 平衡混合效应方差分析模型的协方差阵具有一定结构. 对这类模型, 文献[1]提出了参数估计的一种新方法, 称为谱分解法. 新方法的突出特点是, 能同时给出固定效应和方差分量的估计, 前者是线性的, 后者是二次的,且相互独立. 而后, 文献[2--9]证明了谱分解估计的进一步的统计性质, 同时给出了协方差阵对应的估计, 它不仅是正定阵, 而且可获得它的风险函数, 这些文献还研究了谱分解估计与方差分析估计, 极大似然估计, 限制极大似然估计以及最小范数二次无偏估计的关系. 本文综述这一方向的部分研究成果, 并提出一些待进一步研究的问题.     

Modelling and hierarchical diagnosis of timed discrete-event systems

A new diagnostic method for hierarchically structured discrete-event systems is presented. The efficiency of this method results from the fact that the complexity of the diagnostic task is reduced by first detecting a faulty component using a coarse process model on a high level of abstraction, and subsequently refining the result by investigating the faulty component with the help of a detailed component model in order to identify the fault with sufficient precision. On both abstraction levels, the method uses a timed discrete-event model of the appropriate part of the system. On the higher abstraction level a timed event graph is used that describes how the temporal distance of the events is changed by component faults. On the lower level of abstraction, timed automata are used to cope with the non-determinism of the event sequence generated by the faulty and faultless components. The approach is illustrated by the diagnosis of a batch process.     

Robust state estimation and fault diagnosis for uncertain hybrid nonlinear systems

Robust state estimation and fault diagnosis are challenging problems in the research of hybrid systems. In this paper, a novel robust hybrid observer is proposed for a class of uncertain hybrid nonlinear systems with unknown mode transition functions, model uncertainties and unknown disturbances. The observer consists of a mode observer for discrete mode estimation and a continuous observer for continuous state estimation. It is shown that the mode can be identified correctly and the continuous state estimation error is exponentially uniformly bounded. Robustness to unknown transition functions, model uncertainties and disturbances can be guaranteed by disturbance decoupling and selecting proper thresholds. The transition detectability and mode identifiability conditions are rigorously analyzed. Based on the robust hybrid observer, a robust fault diagnosis scheme is presented for faults modeled as discrete modes with unknown transition functions, and the analytical properties are investigated. Simulations of a hybrid three-tank system demonstrate that the proposed approach is effective.     

基于GA-PLS和AdaBoost的故障诊断方法

提出一种基于GA-PLS和AdaBoost的液压系统故障诊断方法。该方法用遗传算法与偏最小二乘法相结合(Genetic algorithm-partial least squares,GA-PLS)的算法对初始特征向量进行筛选,提取出与故障信息相关程度高的特征向量,把该特征向量作为输入,运用AdaBoost(Adaptive Boost)方法建立分类器,以识别液压系统的工作状态和故障类型。对实验数据分析的结果说明,该方法能准确地选择出特征向量,并有效地应用于液压系统的故障诊断。     

Ellipsoidal Approach to Box-Constrained Quadratic Problems

We present a new heuristic for the global solution of box constrained quadratic problems, based on the classical results which hold for the minimization of quadratic problems with ellipsoidal constraints. The approach is tested on several problems randomly generated and on graph instances from the DIMACS challenge, medium size instances of the Maximum Clique Problem. The numerical results seem to suggest some effectiveness of the proposed approach.     

Mixed-integer quadratic programming

This paper considers mixed-integer quadratic programs in which the objective function is quadratic in the integer and in the continuous variables, and the constraints are linear in the variables of both types. The generalized Benders' decomposition is a suitable approach for solving such programs. However, the program does not become more tractable if this method is used, since Benders' cuts are quadratic in the integer variables. A new equivalent formulation that renders the program tractable is developed, under which the dual objective function is linear in the integer variables and the dual constraint set is independent of these variables. Benders' cuts that are derived from the new formulation are linear in the integer variables, and the original problem is decomposed into a series of integer linear master problems and standard quadratic subproblems. The new formulation does not introduce new primary variables or new constraints into the computational steps of the decomposition algorithm.The author wishes to thank two anonymous referees for their helpful comments and suggestions for revising the paper.     

An interactive model for the layout of buildings

The TOPAZ model and its derivatives, TOPLAY and TOPMET which have been developed for sketch planning design of buildings, are presented and their use is illustrated by application to a 40 000 m² insurance building. TOPMET introduces a completely new approach to solve the inherently difficult quadratic assignment layout problem. The new approach is relatively simple and readily adaptable to microcomputer usage in an interactive form using colour graphics. It also exhibits artificial intelligence behaviour and offers further development possibilities. The paper concludes that while the sketch planning phase of multi-storey building design has received relatively little attention from computer modellers to date, the use of models such as those described in conjunction with inexpensive colour graphic microcomputers is now set for extensive development and application.     

Fault diagnosis for a class of nonlinear uncertain hybrid systems

This paper presents a fault diagnosis architecture for a class of hybrid systems with nonlinear uncertain time-driven dynamics, measurement noise, and autonomous and controlled mode transitions. The proposed approach features a hybrid estimator based on a modified hybrid automaton framework. The fault detection scheme employs a filtering approach that attenuates the effect of the measurement noise and allows tighter mode-dependent thresholds for the detection of both discrete and parametric faults while guaranteeing no false alarms due to modeling uncertainty and mode mismatches. Both the hybrid estimator and the fault detection scheme are linked with an autonomous guard events identification (AGEI) scheme that handles the effects of mode mismatches due to autonomous mode transitions and allows effective mode estimation. Finally, the fault isolation scheme anticipates which fault events may have occurred and dynamically employs the appropriate isolation estimators for isolating the fault by calculating suitable thresholds and estimating the parametric fault magnitude through adaptive approximation methods. Simulation results from a five-tank hybrid system illustrate the effectiveness of the proposed approach.     

输电系统的部分可观Petri网故障诊断器设计

采用部分可观Petri网的故障诊断方法来解决变电站输电系统中不可观事件和不可观运行状态的故障诊断问题.首先,将系统可观测序列分解为长度为1的基础观测序列,应用线性不等式矩阵计算与基础观测序列相符的点火序列集;然后,基于整数线性规划问题,利用向前向后函数拓宽诊断区间,同时应用参数K限定故障诊断序列长度,通过分析系统可观事件和系统部分可观状态,给出故障诊断结果.最后,构造变电站输电系统的部分可观Petri网模型,应用提出的故障诊断算法对输电系统进行诊断,诊断结果准确给出了故障发生与否及故障发生位置.算法适用于在线故障诊断,计算复杂性线性相关于观序列长度.     

Likelihood-based confidence bands for fault lines in response surfaces

 We consider the problem of constructing asymptotic confidence bands, both pointwise and simultaneous, for a smooth fault line in a response surface when the design is represented by a point process, either deterministic or stochastic, with intensity n diverging to infinity. The estimator of the fault line is defined as the ridge line on the likelihood surface which arises from locally fitting a model that employs a linear approximation to the fault line, to a kernel smooth of the data. The construction is based on analysis of the limiting behaviour of perpendicular distance from a point on the true fault line to the nearest point on the ridge. We derive asymptotic properties of bias, and the limiting distribution of stochastic error. This distribution is given by the location of the maximum of a Gaussian process with quadratic drift. Although the majority of attention is focused on the regression problem, the limiting distribution is shown to have wider relevance to local-likelihood approaches to fault line estimation for density or intensity surfaces. Received: 20 August 2000 / Revised version: 27 September 2001 / Published online: 22 August 2002     

Diagnosability of regular systems

A novel approach aimed at evaluating the diagnosability of regular systems under the PMC model is introduced. The diagnosability is defined as the ability to provide a correct diagnosis, although possibly incomplete. This concept is somehow intermediate between one-step diagnosability and sequential diagnosability. A lower bound to diagnosability is determined by lower bounding the minimum of a “syndrome-dependent” bound t_σ over the set of all the admissible syndromes. In turn, t_σ is determined by evaluating the cardinality of the smallest consistent fault set containing an aggregate of maximum cardinality. The new approach, which applies to any regular system, relies on the “edge-isoperimetric inequalities” of connected components of units declaring each other non-faulty. This approach has been used to derive tight lower bounds to the diagnosability of toroidal grids and hypercubes, which improve the existing bounds for the same structures.