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.     

Detection,isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

The goal in many fault detection and isolation schemes is to increase the isolation and identification speed. This paper, presents a new approach of a nonlinear model based adaptive observer method, for detection, isolation and identification of actuator and sensor faults. Firstly, we will design a new method for the actuator fault problem where, after the fault detection and before the fault isolation, we will try to estimate the output of the instrument. The method is based on the formation of nonlinear observer banks where each bank isolates each actuator fault. Secondly, for the sensor problem we will reformulate the system by introducing a new state variable, so that an augmented system can be constructed to treat sensor faults as actuator faults. A method based on the design of an adaptive observers’ bank will be used for the fault treatment. These approaches use the system model and the outputs of the adaptive observers to generate residues. Residuals are defined in such way to isolate the faulty instrument after detecting the fault occurrence. The advantages of these methods are that we can treat not only single actuator and sensor faults but also multiple faults, more over the isolation time has been decreased. In this study, we consider that only abrupt faults in the system can occur. The validity of the methods will be tested firstly in simulation by using a nonlinear model of waste water treatment process with and without measurement noise and secondly with the same nonlinear model but by using this time real data.     

Hybrid modeling based double-granularity fault detection and diagnosis for quadrotor helicopter

Fault detection and diagnosis (FDD) is an effective technology to assure the safety and reliability of quadrotor helicopters. However, there are still some unsolved problems in the existing FDD methods, such as the trade-offs between the accuracy and complexity of system models used for FDD, and the rarely explored structure faults in quadrotor helicopters. In this paper, a double-granularity FDD method is proposed based on the hybrid modeling of a quadrotor helicopter which has been developed in authors’ previous work. The hybrid model consists of a prior model and a set of non-parametric models. The coarse-granularity-level FDD is built on the prior model which can isolate the faulty channel(s); while the fine-granularity-level FDD is built on the nonparametric models which can isolate the faulty components in the faulty channel. In both coarse and fine granularity FDD procedures, principal component analysis (PCA) is adopted for online fault detection. Using such a double-granularity scheme, the proposed FDD method has inherent ability in detecting and diagnosing structure faults or failures in quadrotor helicopters. Experimental results conducted on a 3-DOF hover platform can demonstrate the feasibility and effectiveness of the proposed hybrid modeling technique and the hybrid model based FDD method.     

一种快速有效的供电恢复算法

故障后的供电恢复问题是一个多目标、多约束的优化问题。论文利用一种快速有效的搜索办法建立起满足配电网电流、电压约束的供电恢复方案候选集。并按照开关操作教、负荷转移量、用户优先级等准则，利用定位有序树进行评估，从而为操作人员提供最优恢复方案。对算例的验算结果表明了方法的有效性^[8]。     

A High Token Indicativity Account of Knowledge

In this paper, I provide a probabilistic account of factual knowledge, based on the notion of chance, which is a function of an event (or a fact — I will use ‘fact’ to cover both) given a prior history. This account has some affinity with my chance account of token causation, but it neither relies on it nor presupposes it. Here, I concentrate on the core cases of perceptual knowledge and of knowledge by memory (based on perception). (The account can be extended to the other modes of knowledge, but not in this paper.) The analysis of knowledge presented below is externalist. The underlying intuition guiding the treatment of knowledge in this paper is that knowledge boils down to high token discriminative indicativeness. Type indicativeness or type discriminability are neither necessary nor sufficient for knowledge: the token aspect comes out in the strong dependence on the specific circumstances and chances of the case. The main condition of the first section, the indicativity condition (KI), properly refined, yields pertinent (token) indicativity as a main constituent. Very roughly, it involves the chance of the content clause p being higher given the subject's believing that p than otherwise. The discriminability condition in question (section 3) captures the sense of discriminability appropriate for knowledge and yield the indicativity condition: it is an extension of the indicativity condition KI. Roughly, the subject’s ability to discriminate the object in front of her being red from its being green is captured by holding fixed, in the indicativity condition, the condition “the object in front of her is red or green.” A major element in the analysis is the so-called Contrast Class, which governs the scope of discriminability. This is the class of features that have to be taken into account in the discriminability condition, and it is characterized by two constraints. Very roughly, according to the first constraint, for a feature to be in the contrast class, it must not represent a sub-type of (the feature specified by) the predicate in the content clause. According to the second constraint, which is a central condition with many implications, the chance that the object specified in the content clause has a feature represented in the contrast class must not under the circumstances be too low. This constraint, within the framework of the discriminability condition, brings out a major constitutive aspect of knowledge: knowledge amounts to a limited vulnerability to mistakes of the belief in question under the circumstances at hand. The contrast class plays a major role in my treatment of skepticism. The second constraint on the Contrast Class together with the VHP condition below bring out precisely the way in which perceptual knowledge is fallible.     

On diagnosability of large multiprocessor networks

We consider problems of fault diagnosis in multiprocessor systems. Preparata, Metze and Chien [F.P. Preparata, G. Metze, R.T. Chien, On the connection assignment problem of diagnosable systems, IEEE Trans. Comput. EC 16 (12) (1967) 848-854] introduced a graph theoretical model for system-level diagnosis, in which processors perform tests on one another via links in the system. Fault-free processors correctly identify the status of tested processors, while the faulty processors can give arbitrary test results. The goal is to identify faulty processors based on the test results. A system is said to be t-diagnosable if faulty units can be identified, provided the number of faulty units present does not exceed t. We explore here diagnosis problems for n-cube systems and give bounds for diagnosability of the n-cube. We also describe a simple diagnosis algorithm A which is linear in time and which can be used for sequential diagnosis as well as for incomplete diagnosis in one step. In particular, the algorithm applied to arbitrary topology based interconnection systems G with N processors improves previously known ones. It has sequential diagnosability , which is optimal in the worst case.     

Robust distributed state estimation for genetic regulatory networks with markovian jumping parameters

In this paper, the robust distributed state estimation problem is dealt with for the delayed genetic regulatory networks (GRNs) with SUM logic and multiple sensors. The system parameters are time-varying, norm-bounded, and controlled by a Markov Chain. Time delays here are assumed to be time-varying and belong to the given intervals. The genetic regulatory functions are supposed to satisfy the sector-like condition. We aim to design a distributed state estimator which approximates the genetic states through the measurements of the sensors, i.e., the estimation error system is robustly asymptotically stable in the mean square. Based on the Lyapunov functional method and the stochastic analysis technique, it is shown that if a set of linear matrix inequalities (LMIs) are feasible, the desired distributed state estimator does exist. A numerical example is constructed in the end of the paper to demonstrate the effectiveness of the obtained criteria.     

Approximating scattered data with discontinuities

A method is presented for approximating scattered data by a function defined on a regular two-dimensional grid. It is required that the approximation is discontinuous across given curves in the parameter domain known as faults. The method has three phases: regularisation, local approximation and extrapolation. The main emphasis is put on the extrapolation which is based on a matrix equation which minimises second order differences. By approximating each fault by a set of line segments parallel with one of the axes, it is simple to introduce natural boundary conditions across the faults. The resulting approximation has, as expected, discontinuities across faults and is smooth elsewhere. The method is stable even for large data sets.This research was supported by the Royal Norwegian Council for Scientific and Industrial Research.     

Modeling Discrete Event Systems With Faults Using a Rules-based Modeling Formalism

Obtaining accurate models of systems which are prone to failures and breakdowns is a difficult task. In this paper we present a methodology which makes the task of modeling failure prone discrete event systems (DESs) considerably less cumbersome, less error prone, and more user-friendly. The task of obtaining commonly used automata models for DESs is non-trivial for most practical systems, owing to the fact that the number of states in the commonly used automata models is exponential in the number of signals and faults. In contrast a model of a discrete event system, in the rules based modeling formalism proposed by the co-authors of this paper, is of size polynomial in the number of signals and faults. In order to model failures, we augment the signals set of the rules based formalism to include binary valued fault signals, the values representing either a non-faulty or a faulty state of a certain failure type. Addition of new fault signals requires introduction of new rules for the added fault signal events, and also modification of the existing rules for non-fault events. The rules based modeling formalism is further extended to model real-time systems, and we apply it to model delay-faults of the system as well. The model of a failure prone DES in the rules based can automatically be converted into an equivalent (timed)-automaton model for a failure analysis in the automaton model framework.     

单圈图生成的凯莱图UG_n在PMC模型和MM~*模型下的1好邻诊断度

多处理系统的诊断度是一个重要的研究课题.一种新的系统故障诊断方法称为g好邻诊断度,它是限制每个无故障点至少包含g个无故障的邻点.单圈图生成的凯莱图UG_n作为一种极好的互联网络拓扑结构有许多好的性质.现证明了当n≥4时,单圈图生成的凯莱图UG_n在PMC模型下的1好邻诊断度是2n-1;当n≥5时,UG_n在MM~*模型下的1好邻诊断度是2n-1.     

基于故障树和混沌粒子群算法的锻压机床故障诊断方法

锻压机床由于生产效率高和材料利用率高的特点,被广泛应用于各领域.然而,锻压机床发生故障时,其故障种类繁多、故障数据量大,所以对锻压机床故障源的快速、准确诊断较困难.针对该问题,文章提出一种将故障树分析法和混沌粒子群算法相融合的方法,对锻压机床的故障源进行故障诊断.该方法是先通过故障树分析法对锻压机床的故障进行分析从而得到故障模式及其故障概率,然后由得到的故障模式和已知的故障维修经验分析归纳出故障模式的学习样本,再根据得到的故障概率运用混沌粒子群算法的遍历性快速、准确地诊断出锻压机床发生故障的精确位置.文章提出的方法以锻压机床的伺服系统为例进行了故障诊断实验,将该实验结果与遗传算法、粒子群算法进行对比.实验结果表明,文章的算法在锻压机床伺服系统的故障诊断中准确度更高、速度更快.     

A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator

The rolling bearings often suffer from compound faults in practice. The concurrence of different faults increases the fault detection difficulty and the decoupling detection of compound faults is attracting considerable attentions. Recent publications report the application of the multiwavelets and empirical mode decomposition (EMD) for compound faults decoupling. However, due to limited adaptability they would induce mode mixing or/and overestimation problems in the signal processing. Particularly, the mode mixing would greatly degrade their performance on compound faults detection. To address this issue, this work presents a new method based on the empirical wavelet transform-duffing oscillator (EWTDO) for compound faults decoupling diagnosis of rolling bearings. The empirical wavelet transform (EWT) is able to extract intrinsic modes of a signal by fully adaptive wavelet basis. Hence, the mode mixing and overestimation can be resolved in decoupling processing and the compound faults can be correctly decomposed into different single faults in the form of empirical modes. Then, each single fault frequency was incorporated into a duffing oscillator to establish its corresponding fault isolator. By directly observing the chaotic motion from the Poincar mapping of the isolator outputs the single faults were identified one by one from the empirical modes. Experimental tests were carried out on a rolling bearing fault tester to examine the efficacy of the proposed EWTDO method on compound faults detection. The analysis results show attractive performance with respect to existing decoupling approaches based on the multiwavelets and EMD. In particular, our proposed method is much more reliable in decoupling the compound faults. Hence, the proposed method has practical importance in compound faults decoupling diagnosis for rolling bears.     

STABILIZATION OF VIBRATING BEAM BY VELOCITY FEEDBACK CONTROL

1IntroductionInrecentyearstherehasbeenmuchinterestintopicofcontrolandstabilizationofflexiblevibratingsystemdescribedbyaEuler-Bernoullibeamequationasfollowing(See[1]-[8]).Thequestionofstabilizationofsystem(1.O)hasbeenstudiedbymanyauthors.Forexample,seeLagnese[1],Chen.et.al[2],R.b.,b.,l3]forstabilization,Lagllese[5]forconcentratedS/A'sstabilization.Letusmentionthatthesepapersstudyasymptoticoruniformdecayforthecollsideredsystem,butnotprovetheoptimalityofthedecayrate.C..,.d[8]studytheoptimali…     

基于集对分析的水库诱发地震综合风险评价模型

在水库诱发地震综合风险评价指标体系建立的基础上,根据评价指标的物理含义及其对水库诱发地震的作用情况,建立了水库诱发地震综合风险评价等级标准.用基于加速遗传算法的模糊层次分析法确定水库诱发地震综合风险评价系统中各指标和各子系统的权重,建立了基于集对分析的水库诱发地震综合风险评价模型(SPA-IRAM).SPA-IRAM在克孜尔诱发地震综合风险评价中的应用结果表明:基于集对分析的级别特征值法和属性评判法的评价结果具有一致性和互补性,联合应用可保障SPA-IRAM评价结果的可靠性;该水库诱发地震综合风险评价等级接近于2到3级、介于弱险与中险之间,需要密切监测该水库所在地区的断层活动、水库周围断裂发育和库区岩层裂隙发育的情况,进一步提高水库所在地区人均GDP和水库所在地区钢筋混凝土房屋比例,降低第一产业产值在国民生产总值中的比重和水库所在地区砖木房屋比例.SPA-IRAM综合利用了评价指标、子系统和样本与评价等级标准间联系数的丰富的结构信息和层次信息,可从指标、子系统和样本3个层次定量地分析水库诱发地震综合风险的复杂状态,在水库诱发地震风险管理中具有重要意义.     

A new nonlinear blind source separation method with chaos indicators for decoupling diagnosis of hybrid failures: A marine propulsion gearbox case with a large speed variation

The normal operation of propulsion gearboxes ensures the ship safety. Chaos indicators could efficiently indicate the state change of the gearboxes. However, accurate detection of gearbox hybrid faults using Chaos indicators is a challenging task and the detection under speed variation conditions is attracting considerable attentions. Literature review suggests that the gearbox vibration is a kind of nonlinear mixture of variant vibration sources and the blind source separation (BSS) is reported to be a promising technique for fault vibration analysis, but very limited work has addressed the nonlinear BSS approach for hybrid faults decoupling diagnosis. Aiming to enhance the fault detection performance of Chaos indicators, this work presents a new nonlinear BSS algorithm for gearbox hybrid faults detection under a speed variation condition. This new method appropriately introduces the kernel spectral regression (KSR) framework into the morphological component analysis (MCA). The original vibration data are projected into the reproducing kernel Hilbert space (RKHS) where the instinct nonlinear structure in the original data can be linearized by KSR. Thus the MCA is able to deal with nonlinear BSS in the KSR space. Reliable hybrid faults decoupling is then achieved by this new nonlinear MCA (NMCA). Subsequently, by calculating the Chaos indicators of the decoupled fault components and comparing them with benchmarks, the hybrid faults can be precisely identified. Two specially designed case studies were implemented to evaluate the proposed NMCA-Chaos method on hybrid gear faults decoupling diagnosis. The performance of the NMCA-Chaos was compared with state of art techniques. The analysis results show high performance of the proposed method on hybrid faults detection in a marine propulsion gearbox with large speed variations.     

The impulse influence function for de-centralized control

Impulse influence matrix function is introduced based on that the de-centralized control analysis is analogous to the sub-structural analysis in structural mechanics. The static sub-structural analysis is analogous to the usual de-centralized control, whereas the dynamic sub-structural analysis corresponds to the de-centralized control theory. The reciprocal symmetry for the impulse influence matrix function is proved, and is solved by the precise integration method for time invariant system, giving the results up to computer precision. Based on the impulse influence functions of subsystems, the combination of subsystems can lead to a set of integral equations and be solved numerically. Numerical example demonstrates the effectiveness of the method.     

A low-order active fault-tolerant state space self-tuner for the unknown sampled-data nonlinear singular system using OKID and modified ARMAX model-based system identification

In this paper, we present two control schemes for the unknown sampled-data nonlinear singular system. One is an observer-based digital redesign tracker with the state-feedback gain and the feed-forward gain based on off-line observer/Kalman filter identification (OKID) method. The presented control scheme is able to make the unknown sampled-data nonlinear singular system to well track the desired reference signal. The other is an active fault tolerance state-space self-tuner using the OKID method and modified autoregressive moving average with exogenous inputs (ARMAX) model-based system identification for unknown sampled-data nonlinear singular system with input faults. First, one can apply the off-line OKID method to determine the appropriate (low-) order of the unknown system order and good initial parameters of the modified ARMAX model to improve the convergent speed of recursive extended-least-squares (RELS) method. Then, based on modified ARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown sampled-data nonlinear singular system with immeasurable system state. Moreover, in order to overcome the interference of input fault, one can use a fault-tolerant control scheme for unknown sampled-data nonlinear singular system by modifying the conventional self-tuner control (STC). The presented method can effectively cope with partially abrupt and/or gradual system input faults. Finally, some illustrative examples including a real circuit system are given to demonstrate the effectiveness of the presented design methodologies.     

Optimal actuators placements for the active control of flexible structures

A scheme for the optimal spatial placement of a limited number of sensors and actuators under a minimum energy requirement for the active control of flexible structures is proposed. The method is based on the interpretation of the functional relationship (transfer matrix/conrol influence matrix) between the actuators and modes of the structural system. It is shown that, from the form of the matrix, the controllability and observability of the system with respect to differing locations of the sensors and actuators can be established. The algorithm presented circumvents prevailing problems encountered in contemporary optimal control applications. In particular, and in order to enhance the results presented in this paper, numerical simulation for a prismatic beam subjected to horizontal random wind loads and a simply supported square plate modelled as a single degree of freedom system are given to illustrate the placement strategy.     

The impulse influence function for de-centralized control

Impulse influence matrix function is introduced based on that the de-centralized control analysis is analogous to the sub-structural analysis in structural mechanics. The static sub-structural analysis is analogous to the usual de-centralized control, whereas the dynamic sub-structural analysis corresponds to the de-centralized control theory. The re-     

Dynamical level set method for parameter identification of nonlinear parabolic distributed parameter systems

This article considers a dynamical level set method for the identification problem of the nonlinear parabolic distributed parameter system, which is based on the solvability and stability of the direct PDE (partial differential equation) in Sobolev space. The dynamical level set algorithms have been developed for ill-posed problems in Hilbert space. This method can be regarded as a asymptotical regularization method as long as a certain stopping rule is satisfied. Hence, the convergence analysis of the method is established similar to the proof of convergence of asymptotical regularization. The level set converges to a solution as the artificial time evolves to infinity. Furthermore, the proposed level set method is proved to be stable by using Lyapunov stability theorem, which is constructed in my previous article.Numerical tests are discussed to demonstrate the efficacy of the dynamical level set method, which consequently confirm the level set method to be a powerful tool for the identification of the parameter.