Engine Fault Analysis: Part II?Parameter Estimation Approach

The general fault analysis problem can be divided into two parts: fault detection and diagnosis (location). Fourier series, autocorrelation, and other techniques have been used for fault detection. However, these approaches cannot be utilized for locating the faults. In this paper a methodology is presented to locate faulty cylinder(s). The procedure involves the development of a mathematical model of the engine dynamics. This model takes into consideration the cylinder gas pressure, engine inertia, and load. The resultant torque is computed by using parameter estimation techniques. The parameter estimation technique employed can determine time-varying parameters without prior knowledge of the structure of the parameter. In the problem at hand, this is an important requirement. The resultant torque is the net of the cylinder gas torque and the frictional torque. The model and the estimation procedure have been verified by performing tests on a single-cylinder engine. A discriminant function has been defined to classify the performance of each cylinder. Our results indicate that the amplitude of the resultant torque can be used to identify the faulty cylinder(s). We have verified this approach by tests and studies on a six-cylinder engine. In our experiments we have studied cases involving one or two faulty cylinders.     

智能分布式通信网告警相关性模型及实现

该文提出了智能通信网络的告警相关性分析模型。将通信网按照功能划分成不同的功能子网,针对不同网络的特点,选择适应各自网络特性的告警相关性方法,并建立了层间的网络故障传播模型。提出基于CORBA技术的分布式告警相关性模型。最后,在SDH over DWDM光传送网中,具体分析了告警相关性模型的实现和性能比较。实验证明,采用分布式的告警相关性模型可以有效提高系统的故障诊断水平。     

基于集员未知输入观测器的风力机桨距执行器故障诊断

风力机一般放置在恶劣的环境中,其桨距执行器极易出现故障。文中针对一类含有未知但有界干扰和噪声的风力机系统的桨距执行器故障问题,设计了集员未知输入观测器对桨距的执行器故障进行检测并分离。采用气动机理和现代辨识原理建立风力机系统模型,通过优化未知输入观测器设计对系统中的干扰解耦,基于中心对称多胞体估计不考虑故障时残差的区间包络,并将其作为残差估计的上下动态阈值,实现状态估计。在上述基础上提出了利用一组集员未知输入观测器进行故障诊断的策略。仿真结果表明,在实验过程中,文中所设计的集员未知输入观测器准确地诊断出了风力机桨距执行器的3阶和5阶线性系统在发生突变故障和缓慢时变故障的时间和位置,证明了所提故障诊断策略的有效性。     

Identification of electrically stimulated quadriceps muscles inparaplegic subjects

This work establishes a method for the noninvasive in vivo identification of parametric models of electrically stimulated muscle in paralyzed individuals, when significant inertial loads and/or load transitions are present. The method used differs from earlier work, in that both the pulse width and stimulus period (interpulse interval) modulation are considered. A Hill-type time series model, in which the output is the product of two factors (activation and torque-angle) is used. In this coupled model, the activation dynamics depend upon velocity. Sequential nonlinear least squares methods are used in the parameter identification. The ability of the model, using identified time-varying parameters, to accurately predict muscle torque outputs is evaluated, along with the variability of the identified parameters. This technique can be used to determine muscle parameter models for biomechanical computer simulations, and for real-time adaptive control and monitoring of muscle response variations such as fatigue     

一种FMCW InSAR系统时变基线估计方法

FMCW InSAR系统由于载机平台高度低,气流扰动严重,位置和姿态变化大,且微机电系统惯性测量单元(MEMS IMU)测量精度低,导致系统出现较大的时变基线,严重影响数字高程模型(DEM)精度。针对这一问题,该文提出了一种FMCW InSAR系统的时变基线估计方法。首先利用双通道单视复图像数据,估计出各个距离门内的时变基线导数,然后建立时变基线的距离向空变模型,利用随机抽样一致性检验的方法对模型进行求解,解得水平和垂直向的时变基线导数值,对其积分得到时变基线值。最后通过FMCW InSAR实际数据对时变基线进行估计,并与高精度位置和姿态测量系统(POS)记录的数据进行对比,验证了所提方法的有效性。     

Identification of Hammerstein systems with time-varying piecewise-linear characteristics

This brief deals with the recursive parameter identification of Hammerstein type nonlinear dynamic systems with time-varying piecewise-linear characteristics. A special form of the Hammerstein model, which is linear in parameters, is incorporated into the recursive least squares identification scheme supplemented with the estimation of model internal variables. This enables online estimation of the linear block parameters, the coefficients determining the partition of nonlinearity subdomains and the corresponding linear segment slopes. An illustrative example is included.     

Using neural networks as a fault detection mechanism in MEMS devices

Micro Electro Mechanical Systems (MEMS) were first proposed about 20 years ago. Today, many different kinds have been fabricated and are used in industry, space and scientific fields. The scale and relative size of analog integrated circuits and non-electrical parts are becoming smaller. As a result, the need for automatic test of MEMS is a critical requirement in MEMS fabrication and maintenance. The rapid progress in the design of these systems has not, however, been accompanied by a similar progress in fault classification technologies. MEMS are naturally very non-linear, complex and multi-domain and systems are fabricated near to each other. A large number of faults of different types may occur. This paper presents a combination of a Competitive Neural Network (CNN) and a Robust Heteroscedastic Probabilistic Neural Network (RHPNN) for fault detection in MEMS. The RHPNN has previously been proposed for analog fault detection. Finding the optimum kernel number in the second layer is a drawback of the RHPNN method. In this paper we have used a CNN for finding the optimum kernel number automatically. In addition, as the simulation results show, the correct fault detection percentage is increased in comparison with the RHPNN alone.     

Analytical analysis and feedback linearization tracking control ofthe general Takagi-Sugeno fuzzy dynamic systems

The Takagi-Sugeno (TS) fuzzy modeling technique, a black-box discrete-time approach for system identification, has widely been used to model behaviors of complex dynamic systems. The analytical structure of TS fuzzy models, however, is unknown, causing at two major problems. First, the fuzzy models cannot be utilized to design controllers of the physical systems modeled. Second, there is no systematic technique for designing a controller that is capable of controlling any given TS fuzzy model to achieve the desired tracking or setpoint control performance. In this paper, we provide solutions to these problems. We have proved that a general class of TS fuzzy models is a nonlinear time-varying ARX (Auto-Regressive with eXtra input) model. We have established a simple condition for analytically determining the local stability of the general TS fuzzy dynamic model. The condition can also be used to analytically check the quality of a TS fuzzy model and invalidate the model if the condition warrants. We have developed a feedback linearization technique for systematically designing an output tracking controller so that the output of a controlled TS fuzzy system of the general class achieves perfect tracking of any bounded time-varying trajectory. We have investigated the stability of the tracking controller and established a condition, in relation to the stability of non-minimum phase systems, for analytically deciding whether a stable tracking controller can be designed using our method for any given TS fuzzy system. Three numerical examples are provided to illustrate the effectiveness and utility of our results and techniques     

Adaptive control of time-varying mechanical systems: analysis andexperiments

A new adaptive controller for time-varying mechanical systems is proposed based on two assumptions: 1) the dynamics of time-varying mechanical systems is derived under the assumption that the generalized constraints on the system do not depend on time but the system parameters, such as masses and payloads are time-varying; and 2) the time-varying parameters are given by a group of known bounded time functions and unknown constants. It is shown that the proposed adaptive controller results in a stable closed-loop system. Further, if the desired trajectory of the system is periodic, a time-scaling technique of mapping one cycle period of the desired trajectory into a unit interval is proposed to provide robustness to the parameter adaptation algorithm. An experimental platform consisting of a two-link robot with a time-varying payload is developed to test the proposed adaptive controller. Comparative experimental results demonstrate the effectiveness of the proposed design     

中性点不接地系统单相接地故障分析与仿真

在小电流接地系统中,当出现单相接地故障的时候,因为它构不成短路回路,负荷电流比接地短路产生的电流大得多,而故障相的对地电压本身又小得多,所以故障相的对地电压以及非故障相的相电压均发生变化,前者降低、后者升高,但线电压却依然是对称的,所以用户依然能够不受影响地使用电能,在该系统下可维持运行1到2小时。因为单相接地是一种比较容易见到的故障类型,所以在该文中,采用Matlab仿真软件,模拟小电流接地系统(这里仅对中性点不接地系统进行分析)单相接地故障,建立仿真模型,通过设置电源、线路等参数以及仿真参数,模拟出了仿真结果及系统各个主要参数的波形图,最后根据仿真结果,得出重要结论。     

Reliability Modeling Using SHARPE

Combinatorial models such as fault trees and reliability block diagrams are efficient for model specification and often efficient in their evaluation. But it is difficult, if not impossible, to allow for dependencies (such as repair dependency and near-coincident-fault type dependency), transient and intermittent faults, standby systems with warm spares, and so on. Markov models can capture such important system behavior, but the size of a Markov model can grow exponentially with the number of components in this system. This paper presents an approach for avoiding the large state space problem. The approach uses a hierarchical modeling technique for analyzing complex reliability models. It allows the flexibility of Markov models where necessary and retains the efficiency of combinatorial solution where possible. Based on this approach a computer program called SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) has been written. The hierarchical modeling technique provides a very flexible mechanism for using decomposition and aggregation to model large systems; it allows for both combinatorial and Markov or semi-Markov submodels, and can analyze each model to produce a distribution function. The choice of the number of levels of models and the model types at each level is left up to the modeler. Component distribution functions can be any exponential polynomial whose range is between zero and one. Examples show how combinations of models can be used to evaluate the reliability and availability of large systems using SHARPE.     

Condition assessment and fault prognostics of microelectromechanical systems

Microelectromechanical systems (MEMS) are used in different applications such as automotive, biomedical, aerospace and communication technologies. They create new functionalities and contribute to miniaturize the systems and reduce their costs. However, the reliability of MEMS is one of their major concerns. They suffer from different failure mechanisms which impact their performance, reduce their lifetime and their availability. It is then necessary to monitor their behavior and assess their health state to take appropriate decision such as control reconfiguration and maintenance. These tasks can be done by using Prognostic and Health Management (PHM) approaches. This paper addresses a condition assessment and fault prognostic method for MEMS. The paper starts with a short review about MEMS and presents some challenges identified and which need to be raised to implement PHM methods. The purpose is to highlight the intrinsic constraints of MEMS from PHM point of view. The proposed method is based on a global model combining both nominal behavior model and degradation model to assess the health state of MEMS and predict their remaining useful life. The method is applied on a microgripper, with different degradation models, to show its effectiveness.     

Diagnosability and Distinguishability Analysis and Its Applications

As systems become more complex, it becomes necessary to understand, simplify, and apply fault diagnosis and fault-tolerant design. Although some graph-theoretical diagnostic models such as self-diagnosis model have been studied, the model can not be applied to most systems due to the assumption that each unit has its own testing capability. This paper presents a graph-theoretical diagnosis model expressed by a set of fallible units, a set of measurements, and an incident matrix indicating binary relation between these two sets. Since this model explicitly separates tested units (fallible units) and testing units (measurements), we can discuss diagnostic aspects from both sides. Diagnosability and distinguishability of the model with multiple faults are discussed from combinatorial point of view. Measures of t-fault diagnosability and t-out-of-s diagnosability which was introduced on the self-diagnosis model are discussed. Conditions for these diagnosabilities are expressed by a topological concept of fault distance. The concept of distinguishability is generalized to multiple fault situations called t-fault distinguishability. A lower bound for the distinguishability is obtained by using fault distance. The new concept of s-distinguishability class (s-dc) is presented. This analysis is recommended in the design of systems to attain a required level of diagnosability and distinguishability as well as in the analysis of present systems to investigate their diagnostic aspects. Two application examples are presented: Diagnosability and distinguishability analysis of error-correcting codes, and design of instrumentation systems of large plants with a required level of diagnosability.     

Fault Diagnosis and Reconfiguration for Multilevel Inverter Drive Using AI-Based Techniques

A fault diagnostic and reconfiguration method for a cascaded H-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques is proposed in this paper. Output phase voltages of the MLID are used as diagnostic signals to detect faults and their locations. It is difficult to diagnose an MLID system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of an MLID system. Multilayer perceptron networks are used to identify the type and location of occurring faults. The principal component analysis is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train an NN, and the reduced noise can improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The proposed network is evaluated with simulation test set and experimental test set. The overall classification performance of the proposed network is more than 95%. A reconfiguration technique is also proposed. The proposed fault diagnostic system requires about six cycles to clear an open-circuit or short-circuit fault. The experimental results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration.     

时变参数比例自适应滤波算法

在免提电话和视频会议系统中,自适应滤波器估计的回声路径通常是稀疏的.改进的比例归一化最小均方(IPNLMS)算法能够加快自适应滤波器在估计稀疏系统时的收敛速度,但与归一化最小均方(NLMS)算法相比,其稳态失调的波动性较大.为了解决这一问题,本文提出了一种时变参数IPNLMS(TV-IPNLMS)算法.该算法根据系统的均方误差(MSE)与噪声功率的比值,使用一个sigmoid函数来调整时变参数的值.该时变参数能够降低IPNLMS算法在滤波器到达稳态时的比例增益.仿真结果表明,时变参数方法能够降低IPNLMS算法稳态失调的波动性.该算法可用于回声消除、主动噪声控制等领域.     

Fault diagnosis of analog circuits

In this paper, various fault location techniques in analog networks are described and compared. The emphasis is on the more recent developments in the subject. Four main approaches for fault location are addressed, examined, and illustrated using simple network examples. In particular, we consider the fault dictionary approach, the parameter identification approach, the fault verification approach, and the approximation approach. Theory and algorithms that are associated with these approaches are reviewed and problems of their practical application are identified. Associated with the fault dictionary approach we consider fault dictionary construction techniques, methods of optimum measurement selection, different fault isolation criteria, and efficient fault simulation techniques. Parameter identification techniques that either utilize linear or nonlinear systems of equations to identify all network elements are examined very thoroughly. Under fault verification techniques we discuss node-fault diagnosis, branch-fault diagnosis, subnetwork testability conditions as well as combinatorial techniques, the failure bound technique, and the network decomposition technique. For the approximation approach we consider probabilistic methods and optimization-based methods. The artificial intelligence technique and the different measures of testability are also considered. The main features of the techniques considered are summarized in a comparative table. An extensive, but not exhaustive, bibliography is provided.     

Test Generation Algorithm for Linear Systems Based on Genetic Algorithm

This paper proposes a test generation algorithm combining genetic algorithm for fault diagnosis on linear systems. Most test generation algorithms just used a single value fault model. This test generation algorithm is based on a continuous fault model. This algorithm can improve the treatment of the tolerance problem, including the tolerances of both normal and fault parameters, and enhance the fault coverage rate. The genetic algorithm can be used to choose the characteristic values. The genetic algorithm can enhance precision of test generation algorithm especially for complex fitness functions derived from complex systems under test. The genetic algorithm can also further improve the fault coverage rate by reducing the loop number of divisions of the initial fault range. The experiments are carried out to show this test generation algorithm with a linear system and an integrated circuit.     

A hidden Markov model-based algorithm for fault diagnosis withpartial and imperfect tests

We present a hidden Markov model (HMM) based algorithm for fault diagnosis in systems with partial and imperfect tests. The HMM-based algorithm finds the most likely state evolution, given a sequence of uncertain test outcomes over time. We also present a method to estimate online the HMM parameters, namely, the state transition probabilities, the instantaneous probabilities of test outcomes given the system state and the initial state distribution, that are fundamental to HMM-based adaptive fault diagnosis. The efficacy of the parameter estimation method is demonstrated by comparing the diagnostic accuracies of an algorithm with complete knowledge of HMM parameters with those of an adaptive one. In addition, the advantages of using the HMM approach over a Hamming-distance based fault diagnosis technique are quantified. Tradeoffs in computational complexity versus performance of the diagnostic algorithm are also discussed     

Boolean Difference Techniques for Time-Sequence and Common-Cause Analysis of Fault-Trees

Fault trees are a major model for the analysis of system reliability. In particular, Boolean difference methods applied to fault trees provide a widely used measure of subsystem criticality. This paper generalizes the fault-tree model to time-varying systems and uses timedependent Boolean differences to analyze such systems. In particular, suitable partial Boolean differences provide maximal and minimal solution sets for sensitization conditions. A method of common-cause failure analysis based on partial time-dependent Boolean differences allows the study of failures due to repeated occurrences, at different times, of the same phenomenon. Such methods generalize to systems with repair, and under certain assumptions of independence, steady-state distributions can be used for the analysis of system faults. These methods are generally useful in reliability and sensitivity analysis.     

Discrete time-scale characterization of wideband time-varying systems

Wideband time-varying systems can be found in many applications, including underwater acoustics and ultra-wideband technologies. The time variation due to Doppler scaling effects, coupled with dispersive scattering due to multipath propagation, can severely limit the performance of wideband systems. Just as the discrete time-frequency model can efficiently improve narrowband processing, a discrete time-scale system characterization is important in processing wideband time-varying systems. In this paper, a time-scale model is proposed as a discrete characterization of wideband time-varying systems. This representation decomposes a wideband system output into discrete time shifts and Doppler scalings on the input signal, weighted by a smoothed and sampled version of the wideband spreading function. The proposed transform-based approach uses the Mellin transform that is inherently matched to scalings to geometrically sample the scale parameter and the Fourier transform to arithmetically sample the time-delay parameter. Using this proposed model, and by properly designing the signaling and reception schemes using wavelet techniques, a joint multipath-scale diversity can be achieved over a dyadic time-scale framework in wideband wireless systems. The simulation results demonstrate that, based on the proposed model, performance can be increased by exploiting the diversity intrinsically afforded by the wideband system.