Schemes for fault identification in communication networks

A single fault in a large communication network may result in a large number of fault indications (alarms) making the isolation of the primary source of failure a difficult task. The problem becomes worse in cases of multiple faults. In this paper we present an approach for modelling the problem of fault diagnosis. We propose a graph based network model that takes into account the dependencies among the different objects in the telecommunication environment and a novel approach to estimate the domain of an alarm. Based on that model, we design an algorithm for fault diagnosis and analyze its performance with respect to the accuracy of the fault hypotheses it provides. We also propose and analyze a fault diagnosis algorithm suitable for systems for which an independent failure assumption is valid. Finally, we examine the importance of the information of dependency between objects for the fault diagnosis process     

基于BP神经网络的导弹故障自动诊断研究

精确制导空空导弹是一个复杂系统,及时、准确的识别和诊断导弹故障是化解风险的重要措施。由于导弹故障模式复杂,对其故障进行识别和诊断的难度很大。文中以某型空空导弹测试问题为例,提出一种基于BP神经网络算法的导弹故障自动识别与诊断技术。通过对导弹测试数据的采集和整理形成数据样本,利用神经网络系统的学习和判断能力自动识别及诊断导弹故障,并使用Matlab神经网络工具箱进行仿真验证。验证结果证明,该技术能够快速、准确的识别和诊断导弹故障。     

Theorems for Fault Collapsing in Combinational Circuits

This paper gives a mathematical approach to fault collapsing based on the stuck-at fault model for combinational circuits. The mathematical structure we work within is a Boolean ring of Boolean functions of several variables. The goal of fault collapsing for a given circuit is to reduce the number of stuck-at faults to be considered in test generation and fault diagnosis. For this purpose we need rules that let us eliminate faults from the considered fault set. In this paper some earlier known rules are proved in the new context, and several new rules are presented and proved. The most important of the new theorems deal with the relationship between stuck-at faults on a fanout stem and the branches. The concept of monotony of Boolean functions appears to be important in most of these new rules. Editor: M. Hsiao Audhild Vaaje received the M.S. degree and the Ph.D. degree in mathematics from University of Oslo in 1971 and 1992, respectively. She is an associate professor of mathematics at Agder University College in Norway, where she has been employed since 1972. She has research interests in mathematics applied to fault detection in digital circuits.     

Nonparametric bootstrapping of the reliability function for multiple copies of a repairable item modeled by a birth process

Nonparametric bootstrap inference is developed for the reliability function estimated from censored, nonstationary failure time data for multiple copies of repairable items. We assume that each copy has a known, but not necessarily the same, observation period; and upon failure of one copy, design modifications are implemented for all copies operating at that time to prevent further failures arising from the same fault. This implies that, at any point in time, all operating copies will contain the same set of faults. Failures are modeled as a birth process because there is a reduction in the rate of occurrence at each failure. The data structure comprises a mix of deterministic & random censoring mechanisms corresponding to the known observation period of the copy, and the random censoring time of each fault. Hence, bootstrap confidence intervals & regions for the reliability function measure the length of time a fault can remain within the item until realization as failure in one of the copies. Explicit formulae derived for the re-sampling probabilities greatly reduce dependency on Monte-Carlo simulation. Investigations show a small bias arising in re-sampling that can be quantified & corrected. The variability generated by the re-sampling approach approximates the variability in the underlying birth process, and so supports appropriate inference. An illustrative example describes application to a problem, and discusses the validity of modeling assumptions within industrial practice.     

Online multiple-model-based fault diagnosis and accommodation

While most research attention has been focused on fault detection and diagnosis, much less research effort has been devoted to failure accommodation. Due to the inherent complexity of nonlinear systems, most model-based analytical redundancy fault diagnosis and accommodation (FDA) studies deal with the linear systems, which are subjected to simple additive or multiplicative faults. This assumption has limited the effectiveness and usefulness in practical applications. In this paper, the online fault accommodation (FA) control problems under multiple catastrophic or incipient failures are investigated. The main interest is focused on dealing with the unanticipated component failures in the most general formulation. Through discrete-time Lyapunov stability theory, the sufficient conditions to guarantee the system online stability and to meet performance criteria under failures are derived. A systematic procedure for proper FA under the unanticipated failures is developed. The approach is to combine the control technique derived from discrete-time Lyapunov theory with the modern intelligent technique that is capable of self-optimization and online adaptation for real-time failure estimation. In addition, a complete architecture of FDA is proposed by incorporating the intelligent fault tolerant control strategy with a cost-effective fault detection scheme and a multiple-model based failure diagnosis process to efficiently handle the false alarms and the accommodation of both the anticipated and unanticipated failures in online situations. The simulation results, including a three-tank benchmark problem, substantiate the feasibility study of the proposed FDA framework and provide a promising potential to spin-off applications in industrial and aerospace engineering.     

Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Due to the wide range of critical applications and resource constraints, sensor node gives unexpected responses, which leads to various kind of faults in sensor node and failure in wireless sensor networks. Many research studies focus only on fault diagnosis, and comparatively limited studies have been conducted on fault diagnosis along with fault tolerance in sensor networks. This paper reports a complete study on both 2 aspects and presents a fault tolerance approach using regressional learning with fault diagnosis in wireless sensor networks. The proposed method diagnose the different types of faulty nodes such as hard permanent, soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The experimental evaluation of the fault tolerance module shows promising results with R² of more than 0.99. For the periodic fault such as intermittent fault, the proposed method also predict the possible occurrence time and its duration of the faulty node, so that fault tolerance can be achieved at that particular time period for better performance of the network.     

Failure Analysis of Memory Organization for Utilization in a Self-Repair Memory System

Traditional approaches to memory reliability have been limited to complete redundancy or coding techniques. Redundancy frequently proves too expensive (introducing additional systems faults) and the traditional memory coding techniques have been limited to those areas of memory where a single fault results in a single failure (e.g., a broken core in a magnetic memory) as distinguished from an address decoder fault. To take an integrated approach to this problem, using a variety of coding and modularization techniques on each of the memory subsystems, it is necessary to determine the types of faults and failures caused by these faults that could occur in the system. This paper presents the results of a failure analysis study of typical 2D, 2?D, and 3D memory organizations. Two-way memories are also considered. This study demonstrates that a 2D memory, utilizing a switching array for memory access, is less susceptible to eatastrophic failures than other organizations considered. A memory organization capable of distributing the failures, in a manner permitting correction by linear codes, was adopted. Other techniques for automatic replacement of fault units are also considered.     

贝叶斯估值理论在小子样批次设备检测中的应用

本文针对机载无线电设备故障诊断定位率低的问题，提出利用先验故障信息指导批次定检的思想,通过研究批次定检设备的失效规律，运用贝叶斯理论估计小子样定检设备各模块的故障概率，并且提出了故障检测的4种指导方法．仿真试验结果表明可显著提高故障的一次定位准确率，减少故障查找时间，提高故障诊断效率．     

Diagnosis of Incipient Faults in Weak Nonlinear Analog Circuits

Aiming at the problem to diagnose incipient faults in weak nonlinear analog circuits, an approach is presented in this paper. The approach calculates the fractional Volterra correlation functions beforehand. The next step is to use the fractional Volterra correlation functions and different angle parameters of the fractional wavelet packet transform (FRWPT) to extract the fault signatures. Meanwhile, the computational complexity is analyzed. Then the variables of the fault signatures are constructed, which are used to form the observation sequences of the hidden Markov model (HMM). HMM is used to accomplish the fault diagnosis. The simulations show that the presented method can significantly improve the incipient fault diagnosis capability.     

Diagnosis of single stuck-at faults and multiple timing faults in scan chains

A diagnosis technique to locate single stuck-at faults and multiple timing faults in scan chains is presented. This technique applies single excitation (SE) patterns, in which only one bit is flipped in the presence of multiple faults. With SE patterns, the problem of unknown values in scan chains is eliminated. The diagnosis result is therefore deterministic, not probabilistic. In addition to the first fault, this technique also diagnoses the remaining timing faults by applying multiple excitation patterns. Experiments on benchmark circuits show that average diagnosis resolutions are mostly less than five, even for the tenth fault in the scan chain.     

Soft Fault Feature Extraction in Nonlinear Analog Circuit Fault Diagnosis

Aiming at the problem to diagnose soft faults in nonlinear analog circuits, a novel approach to extract fault features is proposed. The approach is based on the Wigner–Ville distribution (WVD) of the subband Volterra model. First, the subband Volterra kernels of the circuit under test are cleared. Then, the subband Volterra kernels are used to obtain the WVD functions. The fault features are extracted from the WVD functions and taken as input data into the hidden Markov model (HMM). Finally, with classification of features using HMMs, the soft fault diagnosis of the nonlinear analog circuit is achieved. The simulations and experiments show that the method proposed in this paper can extract the fault features effectively and improve the fault diagnosis.     

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.     

A survey on fault diagnosis of analog circuits: Taxonomy and state of the art

Due to the wide range of applications of electronic circuits in the recent years, the fault diagnosis in electronic circuits is a foremost problem. The main purpose of the fault diagnosis technique is isolating the faults present in the electronic circuits and also, detecting the fault which affects the safety and performance of the system. For various real-time applications of fault diagnosis, literature presents several techniques for detecting the faults in electronic circuits. In this paper, reviews on the research based on the fault diagnosis techniques which are all gained much attention are comprehended. Accordingly, 114 research papers related to the fault diagnosis are reviewed and analyzed based on the various objectives. In this review, we present the taxonomy of the fault detection in analog circuits and discuss the state of the art algorithms with various advantages and major drawbacks. The comprehensive analysis is carried out on finding the coverage of the publishers, faults, circuits, methods, simulation tools, and metrics. This critical review finally discusses the research challenges that are still available in the existing techniques and the way to extend the current research is also examined.     

TMR voting in the presence of crosstalk faults at the voter inputs

In high reliability systems, the effectiveness of fault tolerant techniques, such as Triple-Modular-Redundancy (TMR), must be validated with respect to the faults that are likely in the current technology. In todays' Integrated Circuits (IC), this is the case of crosstalks, whose importance is growing because of device & interconnect scaling. This paper analyzes the problem of crosstalk faults at the inputs of voters in TMR systems. In particular, possible problems are illustrated, and it is shown that such crosstalk may invalidate the reliability of both voting, and diagnosing operations. The problem is analyzed from a probabilistic point of view. Its occurrence is estimated by using a set of TMR systems obtained with combinational benchmarks as functional modules. The possible problems of such operations are discussed in the presence of crosstalk faults. It is shown that crosstalk may invalidate the reliability of both voting, and diagnosis operations. A probabilistic model of the voting & diagnosis operations in the presence of crosstalk has been developed. Finally, such a model has been used to estimate the probability of voting & diagnosis failures in a set of TMR systems obtained by using combinational benchmarks as functional modules. We have shown that the presence of crosstalk faults at voter inputs may impair both the voting, and the diagnosis mechanisms. This problem has been quantified by applying a probabilistic model of crosstalk fault effects on voting and diagnosis to a set of benchmark circuits. Results show that crosstalk may create a reliability problem for TMR systems. Such a problem can be solved by using on-line testing or design for testability providing additional controllability & observability to the replicated functional units.     

Software fault tolerance: t/(n-1)-variant programming

This paper describes the software fault tolerance scheme, t/(n-1)-variant programming (t/(n-1)-VP), which is based on a particular system diagnosis technique used in hardware and thereby has some spectral advantages involving a simplified adjudication mechanism and enhanced capability of tolerating faults. The dependability of the t/(n-1)-VP architecture is evaluated and then compared with two similar schemes: N-version programming (NVP) and N self-checking programming (NSCP). The comparison shows that t/(n-1)-VP is a viable addition or alternative to present techniques. Much of the classical dependability-analysis of software fault tolerance approaches has focused on the simplest architectural examples that tolerate only single software faults, without considering tolerance to multiple and/or related faults. The results obtained from such analyses are thus restricted. The dependability evaluation in this paper deals with more-complicated and general software redundancy: various architectures tolerating two or more faults. It is no surprise that we came to new conclusions: both t/(n-1)-VP and the NVP scheme have the ability to tolerate some related faults between software variants; in general, t/(n-1)-VP has higher reliability, whereas NVP is better from the safety viewpoint     

Application of a combined active control and fault detection scheme to an active composite flexible structure

In this paper, the problem of increasing reliability of active control procedure is considered. Indeed, a design method of rejection perturbation in presence of potentially faults, on a flexible structure with integrated piezo-ceramics, is presented. The piezo-ceramics are used as actuators and sensors. A single unit based solution, which handles both control action and fault diagnosis is proposed. The algorithm uses H_∞ optimization techniques. A full order model of the structure is first obtained via both finite-element (FE) approach and identification procedure. This model is then reduced in order to be used in our robust approach. By a suitable choice of weightings functions, the provided method is able to reject disturbance robustly and to estimate occurred faults. The case of sensors and actuators faults is discussed. The choice of weightings for diagnosis and control systems is also tackled. Finally, the effectiveness of this integrated method is confirmed by both simulation and experimental results.     

Multi-valued logic mapping of resistive short and open delay-fault testing in deep sub-micron technologies

In advanced technologies an increasing proportion of defects manifest themselves as small delay faults. Most of today’s advanced delay-fault algorithms are able to propagate those delay faults which create logic or glitch faults. An algorithm is proposed for circuit fault diagnosis in deep sub-micron technology to propagate the actual timing faults as well as those delay faults that eventually create logic faults to the primary outputs. Unlike the backtrack algorithm that predicts the fault site by tracing the syndrome at a faulty output back into the circuit, this approach propagates the fault from the fault site by mapping a nine-valued voltage model on top of a five-valued voltage model. In such a forward approach, accuracy is greatly increased since all composite syndromes at all faulty outputs are considered simultaneously. As a result, the proposed approach is applicable even when the delay size is relatively small. Experimental results show that the number of fault candidates produced by this approach is considerable.     

某型炮兵雷达便携式故障诊断系统的设计与实现

某型炮兵雷达自装备部队以来,在使用过程中发现故障现象明显增多,已严重影响了雷达的日常训练和战斗力的形成。针对目前炮兵雷达基层部队技术力量薄弱,故障维修困难等问题,基于专家系统和神经网络技术开发了雷达便携式故障诊断系统,实现了雷达故障的快速诊断及排除,提高了雷达的综合保障能力。探讨了系统的硬件结构、故障诊断架构、故障诊断模型和故障诊断流程。应用结果表明系统的可行性和实用性,极大地提高了维修效率和故障诊断能力。     

Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study

This paper deals with the problem of bearing failure detection and diagnosis in induction motors. Indeed, bearing deterioration is now the main cause of induction motor rotor failures. In this context, two fault detection and diagnosis techniques, namely the Park transform approach and the Concordia transform, are briefly presented and compared. Experimental tests, on a 0.75 kW two-pole induction motor with artificial bearing damage, outline the main features of the aforementioned approaches for small- and medium-size induction motors bearing failure detection and/or diagnosis.