Fault Candidates Research Articles

Certifying Robustness of Separating Inputs and Outputs in Active Fault Diagnosis for Uncertain Nonlinear Systems

To ensure safe operation of technical processes, faults have to be reliably detected and isolated to provide information for process maintenance, shutdown, or reconfiguration. Fault detection and isolation can be achieved by invalidation of fault candidates, i.e. models of the system in fault-free and faulty condition. In order to enhance the performance of fault detection and isolation, so-called active approaches use input signals with the objective that the resulting system outputs are consistent with at most one fault candidate. Guaranteeing or analyzing robustness of active fault diagnosis with respect to input, output, and process uncertainties and nonlinearities is challenging. This paper provides certificates of robustness of input sequences with respect to the aforementioned uncertainties and nonlinearities. The certificates enable the determination of input and output uncertainties for which unique fault diagnosis results can still be guaranteed. In addition, a method is presented to select a minimal number of outputs that still guarantee robust fault diagnosis, thus reducing the measurement setup and cost. The approach employs nonlinear mixed-integer feasibility problems and a relaxation framework and does not require the explicit computation of reachable sets. The approach is applicable to polynomial discrete-time systems and is demonstrated for a numerical example.

Combining Slicing and Constraint Solving for Better Debugging: The CONBAS Approach

Although slices provide a good basis for analyzing programs during debugging, they lack in their capabilities providing precise information regarding the most likely root causes of faults. Hence, a lot of work is left to the programmer during fault localization. In this paper, we present an approach that combines an advanced dynamic slicing method with constraint solving in order to reduce the number of delivered fault candidates. The approach is called Constraints Based Slicing (CONBAS). The idea behind CONBAS is to convert an execution trace of a failing test case into its constraint representation and to check if it is possible to find values for all variables in the execution trace so that there is no contradiction with the test case. For doing so, we make use of the correctness and incorrectness assumptions behind a diagnosis, the given failing test case. Beside the theoretical foundations and the algorithm, we present empirical results and discuss future research. The obtained empirical results indicate an improvement of about 28% for the single fault and 50% for the double-fault case compared to dynamic slicing approaches.

A fault magnitude based strategy for effective fault classification

A common approach in fault diagnosis is monitoring the deviations of measured variables from the values at normal operations to identify the root causes of faults. When the number of conceivable faults is larger than that of predictive variables, conventional approaches can yield ambiguous diagnosis results including multiple fault candidates. To address the issue, this work proposes a fault magnitude based strategy. Signed digraph is first used to identify qualitative relationships between process variables and faults. Empirical models for predicting process variables under assumed faults are then constructed with support vector regression (SVR). Fault magnitude data are projected onto principal components subspace, and the mapping from scores to fault magnitudes is learned via SVR. This model can estimate fault magnitudes and discriminate a true fault among multiple candidates when different fault magnitudes yield distinguishable responses in the monitored variables. The efficacy of the proposed approach is illustrated on an actuator benchmark problem.

Automated design debugging in a testbench-based verification environment

Debugging is one of the major bottlenecks in the current VLSI design process as design size and complexity increase. Efficient automation of debugging procedures helps to reduce debugging time and to increase diagnosis accuracy. This work proposes an approach for automating the design debugging procedures by integrating SAT-based debugging with testbench-based verification. The diagnosis accuracy increases by iterating debugging and counterexample generation, i.e., the total number of fault candidates decreases. The experimental results show that our approach while not requiring a formal specification is as accurate as exact formal debugging in 71% of the experiments.

Minimization of ambiguity in parametric fault diagnosis of analog circuits: A complex network approach

Aiming at the problem to locate parametric faults in analog circuits, we propose a novel procedure for minimizing ambiguity of parametric faults in this paper. We first construct a complex network to describe the way in which candidate fault features and the corresponding voltage response interact. And the network constructed is found to follow a scale-free distribution in its node degree. We indicate this topological property of nodes results from the ambiguity of overlapped parametric faults. Through investigating the complex network with power-law distributions, ambiguity minimizing strategies are proposed to reduce redundant fault features and increase effective fault features while improving the overall level of fault diagnosis. Simulation results show the effectiveness of the method of the parametric fault diagnosis in analog circuit.

A circuit approach to fault diagnosis in power systems by wide area measurement system

Fault diagnosis following a disturbance in a power system is of great importance for the operators in the control center as a prerequisite for system restoration. In this article, for the first time, an analytic method for fault diagnosis, using the wide area measurement system (WAMS) and employing circuit rules, is developed. Instead of conventional information about status of protective relays and circuit breakers, voltage and current phasors at different points of the power network after fault occurrence are utilized. Because most of the power systems will be equipped with WAMS consisting of high-speed sampling features, the proposed method introduces a new application of WAMS for fault diagnosis without the need for additional hardware. In this method, with the aid of network impedance matrix, analytic voltage and current phasors resulted after a fault in each of the fault candidate points are compared with those monitored by WAMS. The faulted point is where the best fitting coincides with the monitored voltage and current phasors. The proposed method is applied on IEEE 118-bus test system where the results confirm its ability in wide-area fault diagnosis of large power networks. Copyright © 2012 John Wiley & Sons, Ltd.

Dynamic fault diagnosis using extended matrix and tensor locality preserving discriminant analysis

It is well acknowledged that utilizing dynamic information can improve accuracy in fault diagnosis for dynamic processes. Conventional methods encode dynamic information by constructing an extended vector comprising current process data as well as past process data. Then the classic Linear Discriminant Analysis (LDA) is usually applied to extended vectors to reduce dimensionality and obtain a discriminative subspace where overlapping among different fault classes is minimized. However, using extended vectors aggravates the “curse of dimensionality” problem and loses structure information in variables. Besides, LDA probably provides suboptimal results when there are more than two candidate fault classes. This paper proposes using extended matrices to encoding dynamic information and using a novel dimensionality reduction method named Tensor Locality Preserving Discriminant Analysis (TLPDA) to perform dimensionality reduction on extend matrices directly. TLPDA is based on local structure in data and overcomes the main drawbacks of LDA. A new dynamic fault diagnosis scheme is developed based on extended matrices and TLPDA. Extensive simulations on the Tennessee Eastman (TE) benchmark simulation process clearly demonstrate the superiority of our methods in terms of misclassification rate and making use of extra training data.

FDI with neural network models of faulty behaviours and fault probability evaluation: Application to DAMADICS

This paper presents a complete fault detection and isolation (FDI) scheme for unknown nonlinear systems. Neural networks models are designed that learn the fault-free and also the faulty behaviours of the considered systems. Then residuals are designed and fault probabilities are introduced for each fault candidate. According to the computation of a confidence factor, the proposed method is also suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems) actuator benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.

Diagnosing Multiple Byzantine Open-Segment Defects Using Integer Linear Programming

Faulty behaviors of open-segment defects are non-deterministic due to the Byzantine effect induced by the physical circuit layout. It is the test pattern and difficult for traditional ATPGs to manifest the corresponding faulty effect. Therefore, we propose a three-stage diagnosis approach for finding multiple open-segment defects. Stage one applies path tracing to help extract candidate fault sites from error outputs of failing patterns. An ILP solver in stage two effectively enumerates all fault combinations when considering fault candidates and simulation responses simultaneously. During stage three, fault simulation with support of physical information is responsible for identifying true open-segment defects by pruning false cases. Experimental results show good resolutions (only 1.7X and 1.5X total numbers of segments on average under 1,000 random and 5-detect patterns, respectively) for all ISCAS’85 circuits with 2–5 randomly-injected open-segment defects.

Deriving Bayesian Classifiers from Flight Data to Enhance Aircraft Diagnosis Models

Online fault diagnosis is critical for detecting the onset and hence the mitigation of adverse events that arise in complex systems, such as aircraft and industrial processes. A typical fault diagnosis system consists of: (1) a reference model that provides a mathematical representation for various diagnostic monitors that provide partial evidence towards active failure modes, and (2) a reasoning algorithm that combines set-covering and probabilistic computation to establish fault candidates and their rankings. However, for complex systems reference models are typically incomplete, and simplify- ing assumptions are made to make the reasoning algorithms tractable. Incompleteness in the reference models can take several forms, such as absence of discriminating evidence, and errors and incompleteness in the mapping between evidence and failure modes. Inaccuracies in the reasoning algorithm arise from the use of simplified noise models and independence assumptions about the evidence and the faults. Recently, data mining approaches have been proposed to help mitigate some of the problems with the reference models and reasoning schemes. This paper describes a Tree Augmented Na ̈ıve Bayesian Classifier (TAN) that forms the basis for systematically extending aircraft diagnosis reference models using flight data from systems operating with and without faults. The performance of the TAN models is investigated by comparing them against an expert supplied reference model. The results demonstrate that the generated TAN structures can be used by human experts to identify improvements to the reference model, by adding (1) new causal links that relate evidence to faults, and different pieces of evidence, and (2) updated thresholds and new monitors that facilitate the derivation of more precise evidence from the sensor data. A case study shows that this improves overall reasoner performance.

Integrated Framework of Probabilistic Signed Digraph Based Fault Diagnosis Approach to a Gas Fractionation Unit

An integrated implementation solution and theoretical framework of fault diagnosis approach based on probabilistic signed digraph (PSDG) is proposed and applied to a gas fractionation unit. On the basis of the primary method of PSDG presented in our previous work, a complete framework of PSDG is constructed, including its definition and reasoning to its implementation. Nodes and branches in PSDG contain uncertain information and their a priori conditional probabilistic parameters are decided by using little knowledge of the studied plant; thus, a PSDG model can be built properly. After cycle processing and model simplification, PSDG reasoning can be conducted approximately on the basis of consistent rule. In implementation of PSDG, the probabilities of candidate faults can be computed and arranged, and the most possible fault is found. Therefore the real fault cause can be further confirmed reasonably. Compared with the conventional qualitative SDG, the qualitative ambiguities in PSDG can be reduced to so...

Generation of candidates' tree for the fault diagnosis of discrete event systems

This paper presents a discrete event model-based approach for Fault Detection and Isolation of manufacturing systems. This approach considers a system as a set of independent Plant Elements (PEs). Each PE is composed of a set of interrelated Parts of Plant (PoPs) modeled by a Moore automaton. Each PoP model is only aware of its local behavior. The degraded and faulty behaviors are added to each PoP model in order to obtain extended PoP ones. An extrapolation of Gaussian learning is realized to obtain acceptable temporal intervals between the time occurrences of correlated events. Finally based on the PoP extended models and the links between them, a fault candidates' tree is established for each plant element. This candidates' tree corresponds to a local on-line fault event occurrence observer, called diagnoser. Thus, the diagnosis decision is distributed on each plant element. An application example is used to illustrate the approach.

The concept of residuals for fault localization in discrete event systems

In this paper an approach for fault localization in closed-loop Discrete Event Systems is proposed. The presented diagnosis method allows fault localization using a fault-free system model to describe the expected system behavior. Via a systematic comparison of the observed and the expected behavior, a fault can be detected and a set of fault candidates is determined. Inspired by residuals known from diagnosis in continuous systems, different set operations are introduced to generate the fault candidate set. After fault detection and a first fault localization, a procedure is given to render the fault localization more precisely by an analysis of the further observed system behavior. Special emphasis is given to the use of identified models for the fault-free system behavior. The approach is explained using a laboratory manufacturing facility.

Alarm System Design Based on Cause-Effect Model

Recently in U.S. and Europe, the approach for the safety of plant intensifies because of many accidents in plant. In the approaches, alarm system is especially important. Alarms should be used to enable operators to diagnose faults of plant and plan countermeasures. To accomplish these functions, nuisance alarms should be eliminated. In this study, we suggest a systematic method of alarm system design for unsteady state process by using Cause-Effect model. The presumed indicators were chosen from intended indicators are theoretically guaranteed to be able to qualitatively distinguish all assumed faults. And by use of the different model for each process in unsteady state operation, it is considered that fault candidates and indicators are restricted. The method is applied to a simple process for the case study.

Reducing the Inaccuracy Caused by Inappropriate Time Window in Probabilistic Fault Localization

To reduce the inaccuracy caused by inappropriate time window, we propose two probabilistic fault localization schemes based on the idea of “extending time window.” The global window extension algorithm (GWE) uses a window extension strategy for all candidate faults, while the on-demand window extension algorithm (OWE) uses the extended window only for a small set of faults when necessary. Both algorithms can increase the metric values of actual faults and thus improve the accuracy of fault localization. Simulation results show that both schemes perform better than existing algorithms. Furthermore, OWE performs better than GWE at the cost of a bit more computing time.

Multiple Faults Isolation for Hybrid Systems with Unknown Fault Pattern

This work is concerned with multiple faults isolation for hybrid systems based on Global Analytical Redundancy Relationships (GARRs) approach. GARRs are derived from the Hybrid Bond Graph (HBG) model of a hybrid system with a specified causality assignment procedure. In this article, multiple faults are considered in a complex hybrid system and these faults can develop during a mode when the faults are not detectable. Once a fault is detected, a fault candidates set is generated from mode dependent-fault signature matrix (MD-FSM) tables and a set of fault pattern hypothesis is created from the fault candidates set for further refinement. Fault isolation is carried out using a multiple nonlinear least square optimization (MNLSO) algorithm. The developed technique can deal with multiple faults with unknown pattern. The fault could be of incipient or abrupt nature. The simulation results show the effectiveness of the proposed method.

Diagnosing multiple intermittent failures using maximum likelihood estimation

In fault diagnosis intermittent failure models are an important tool to adequately deal with realistic failure behavior. Current model-based diagnosis approaches account for the fact that a component c j may fail intermittently by introducing a parameter g j that expresses the probability the component exhibits correct behavior. This component parameter g j , in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on g j is not known a priori. While proper estimation of g j can be critical to diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, coined Barinel, that computes estimations of the g j as integral part of the posterior candidate probability computation using a maximum likelihood estimation approach. Barinel's diagnostic performance is evaluated for both synthetic systems, the Siemens software diagnosis benchmark, as well as for real-world programs. Our results show that our approach is superior to reasoning approaches based on classical persistent failure models, as well as previously proposed intermittent failure models.

Tsunami Simulations of the 1867 Virgin Island Earthquake: Constraints on Epicenter Location and Fault Parameters

Abstract The 18 November 1867 Virgin Island earthquake and the tsunami that closely followed caused considerable loss of life and damage in several places in the northeast Caribbean region. The earthquake was likely a manifestation of the complex tectonic deformation of the Anegada Passage, which cuts across the Antilles island arc between the Virgin Islands and the Lesser Antilles. In this article, we attempt to characterize the 1867 earthquake with respect to fault orientation, rake, dip, fault dimensions, and first tsunami wave propagating phase, using tsunami simulations that employ high-resolution multibeam bathymetry. In addition, we present new geophysical and geological observations from the region of the suggested earthquake source. Results of our tsunami simulations based on relative amplitude comparison limit the earthquake source to be along the northern wall of the Virgin Islands basin, as suggested by Reid and Taber (1920), or on the carbonate platform north of the basin, and not in the Virgin Islands basin, as commonly assumed. The numerical simulations suggest the 1867 fault was striking 120°–135° and had a mixed normal and left-lateral motion. First propagating wave phase analysis suggests a fault striking 300°–315° is also possible. The best-fitting rupture length was found to be relatively small (50 km), probably indicating the earthquake had a moment magnitude of ∼7.2. Detailed multibeam echo sounder surveys of the Anegada Passage bathymetry between St. Croix and St. Thomas reveal a scarp, which cuts the northern wall of the Virgin Islands basin. High-resolution seismic profiles further indicate it to be a reasonable fault candidate. However, the fault orientation and the orientation of other subparallel faults in the area are more compatible with right-lateral motion. For the other possible source region, no clear disruption in the bathymetry or seismic profiles was found on the carbonate platform north of the basin.

Discrete Event Model-Based Approach for Fault Detection and Isolation of Manufacturing Systems

This paper presents a discrete event model-based approach for Fault Detection and Isolation of manufacturing systems. This approach considers a system as a set of independent plant elements. Each plant element is composed of a set of interrelated Parts of Plant (PoPs) modeled by a Moore automaton. Each PoP model is only aware of its local behavior. The degraded and faulty behaviors are added to each PoP model in order to obtain extended PoP ones. An extrapolation of Gaussian learning is realized to obtain acceptable temporal intervals between the time occurrences of correlated events. Finally based on the PoP extended models and the links between them, a fault candidates' tree is established for each plant element. This candidates' tree corresponds to a local on-line fault event occurrence observer, called diagnoser. Thus, the diagnosis decision is distributed on each plant element. An application example is used to illustrate the approach.

A residual inspired approach for fault localization in DES

In this paper an approach for fault localization in Discrete Event Systems (DES) is proposed. The presented diagnosis method allows fault localization using a fault-free nominal system model. Via a systematic comparison of the observed and the expected system behavior, it is possible to determine a set of fault candidates. Inspired by residuals known from diagnosis in continuous systems, different set operations are presented that carry out this comparison. After a fault has been detected and a first estimate concerning its localization has been performed, a special algorithm analyzes the further system behavior in order to determine a more precise fault localization. The algorithm also works on the basis of the nominal system model. The method is explained using a manufacturing system example.