Showing papers on "Fault detection and isolation published in 2001"

Prioritizing test cases for regression testing

Abstract: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection, a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing, the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. We describe several techniques for using test execution information to prioritize test cases for regression testing, including: 1) techniques that order test cases based on their total coverage of code components; 2) techniques that order test cases based on their coverage of code components not previously covered; and 3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites.

A geometric approach to nonlinear fault detection and isolation

Abstract: We present a differential geometric approach to the problem of fault detection and isolation for nonlinear systems. A necessary condition for the problem to be solvable is derived in terms of an unobservability distribution, which is computable by means of suitable algorithms. The existence and regularity of such a distribution implies the existence of changes of coordinates in the state and in the output space which induce an "observable" quotient subsystem unaffected by all fault signals but one. For this subsystem, a fault detection filter is designed.

Reconstruction-Based Fault Identification Using a Combined Index

Abstract: Process monitoring and fault diagnosis are crucial for efficient and optimal operation of a chemical plant. This paper proposes a reconstruction-based fault identification approach using a combined index for multidimensional fault reconstruction and identification. Fault detection is conducted using a new index that combines the squared prediction error (SPE) and T2. Necessary and sufficient conditions for fault detectability are derived. The combined index is used to reconstruct the fault along a given fault direction. Faults are identified by assuming that each fault in a candidate fault set is the true fault and comparing the reconstructed indices with the control limits. Fault reconstructability and identifiability on the basis of the combined index are discussed. A new method to extract fault directions from historical fault data is proposed. The dimension of the fault is determined on the basis of the fault detection indices after fault reconstruction. Several simulation examples and one practical c...

Incorporating varying test costs and fault severities into test case prioritization

Abstract: Test case prioritization techniques schedule test cases for regression testing in an order that increases their ability to meet some performance goal. One performance goal, rate of fault detection, measures how quickly faults are detected within the testing process. In previous work (S. Elbaum et al., 2000; G. Rothermel et al., 1999), we provided a metric, APFD, for measuring rate of fault detection, and techniques for prioritizing test cases to improve APFD, and reported the results of experiments using those techniques. This metric and these techniques, however, applied only in cases in which test costs and fault severity are uniform. We present a new metric for assessing the rate of fault detection of prioritized test cases that incorporates varying test case and fault costs. We present the results of a case study illustrating the application of the metric. This study raises several practical questions that might arise in applying test case prioritization; we discuss how practitioners could go about answering these questions.

Integrated active fault-tolerant control using IMM approach

Abstract: An integrated fault detection, diagnosis, and reconfigurable control scheme based on interacting multiple model (IMM) approach is proposed. Fault detection and diagnosis (FDD) is carried out using an IMM estimator. An eigenstructure assignment (EA) technique is used for reconfigurable feedback control law design. To achieve steady-state tracking, reconfigurable feedforward controllers are also synthesized using input weighting approach. The developed scheme can deal with not only actuator and sensor faults, but also failures in, system components. To achieve fast and reliable fault detection, diagnosis, and controller reconfiguration, new fault diagnosis and controller reconfiguration mechanisms have been developed by a suitable combination of the information provided by the mode probabilities from the IMM algorithm and an index related to the closed-loop system performance. The proposed approach is evaluated using an aircraft example, and excellent results have been obtained.

Concepts and methods in fault-tolerant control

Abstract: Faults in automated processes will often cause undesired reactions and shut-down of a controlled plant, and the consequences could be damage to technical parts of the plant, to personnel or the environment. Fault-tolerant control combines diagnosis with control methods to handle faults in an intelligent way. The aim is to prevent that simple faults develop into serious failure and hence increase plant availability and reduce the risk of safety hazards. Fault-tolerant control merges several disciplines into a common framework to achieve these goals. The desired features are obtained through online fault diagnosis, automatic condition assessment and calculation of appropriate remedial actions to avoid certain consequences of a fault. The envelope of the possible remedial actions is very wide. Sometimes, simple re-tuning can suffice. In other cases, accommodation of the fault could be achieved by replacing a measurement from a faulty sensor by an estimate. In yet other situations, complex reconfiguration or online controller redesign is required. This paper gives an overview of recent tools to analyze and explore structure and other fundamental properties of an automated system such that any inherent redundancy in the controlled process can be fully utilized to maintain availability, even though faults may occur.

A Novel Approach to the Classification of the Transient Phenomena in Power Transformers Using Combined Wavelet Transform and Neural Network

Abstract: The wavelet transform is a powerful tool in the analysis of the power transformer transient phenomena because of its ability to extract information from the transient signals simultaneously in both the time and frequency domain. This paper presents a novel technique for accurate discrimination between an internal fault and a magnetizing inrush current in the power transformer by combining wavelet transforms with neural networks. The wavelet transform is firstly applied to decompose the differential current signals of the power transformer into a series of detailed wavelet components. The spectral energies of the wavelet components are calculated and then employed to train a neural network to discriminate an internal fault from the magnetizing inrush current. The simulated results presented clearly show that the proposed technique can accurately discriminate between an internal fault and a magnetizing inrush current in power transformer protection.

Chaotic resonance — methods and applications for robust classification of noisy and variable patterns

Abstract: A fundamental tenet of the theory of deterministic chaos holds that infinitesimal variation in the initial conditions of a network that is operating in the basin of a low-dimensional chaotic attractor causes the various trajectories to diverge from each other quickly. This "sensitivity to initial conditions" might seem to hold promise for signal detection, owing to an implied capacity for distinguishing small differences in patterns. However, this sensitivity is incompatible with pattern classification, because it amplifies irrelevant differences in incomplete patterns belonging to the same class, and it renders the network easily corrupted by noise. Here a theory of stochastic chaos is developed, in which aperiodic outputs with 1/f2 spectra are formed by the interaction of globally connected nodes that are individually governed by point attractors under perturbation by continuous white noise. The interaction leads to a high-dimensional global chaotic attractor that governs the entire array of nodes. An example is our spatially distributed KIII network that is derived from studies of the olfactory system, and that is stabilized by additive noise modeled on biological noise sources. Systematic parameterization of the interaction strengths corresponding to synaptic gains among nodes representing excitatory and inhibitory neuron populations enables the formation of a robust high-dimensional global chaotic attractor. Reinforcement learning from examples of patterns to be classified using habituation and association creates lower dimensional local basins, which form a global attractor landscape with one basin for each class. Thereafter, presentation of incomplete examples of a test pattern leads to confinement of the KIII network in the basin corresponding to that pattern, which constitutes many-to-one generalization. The capture after learning is expressed by a stereotypical spatial pattern of amplitude modulation of a chaotic carrier wave. Sensitivity to initial conditions is no longer an issue. Scaling of the additive noise as a parameter optimizes the classification of data sets in a manner that is comparable to stochastic resonance. The local basins constitute dynamical memories that solve difficult problems in classifying data sets that are not linearly separable. New local basins can be added quickly from very few examples without loss of existing basins. The attractor landscape enables the KIII set to provide an interface between noisy, unconstrained environments and conventional pattern classifiers. Examples given here of its robust performance include fault detection in small machine parts and the classification of spatiotemporal EEG patterns from rabbits trained to discriminate visual stimuli.

Dual use of superscalar datapath for transient-fault detection and recovery

Abstract: Diminutive devices and high clock frequency of future microprocessor generations are causing increased concerns for transient soft failures in hardware, necessitating fault detection and recovery mechanisms even in commodity processors. In this paper, we propose a fault-tolerant extension for modern superscalar out-of-order datapath that can be supported by only modest additional hardware. In the proposed extensions, error-detection is achieved by verifying the redundant results of dynamically replicated threads of executions, while the error-recovery scheme employs the instruction-rewind mechanism to restart at a failed instruction. We study the performance impact of augmenting superscalar microarchitectures with this fault tolerance mechanism. An analytical performance model is used in conjunction with a performance simulator. The simulation results of 11 SPEC95 and SPEC2000 benchmarks show that in the absence of faults, error detection causes a 2% to 45% reduction in throughput, which is in line with other proposed detection schemes. In the presence of transient faults, the fast error recovery scheme contributes very little additional slowdown.

Automatic feature extraction of waveform signals for in-process diagnostic performance improvement

Abstract: In this paper, a new methodology is presented for developing a diagnostic system using waveform signals with limited or with no prior fault information The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment By using this methodology, a diagnostic system can be developed and its performance is continuously improved as the knowledge of process faults is automatically accumulated during production As a real example, the tonnage signal analysis for stamping process monitoring is provided to demonstrate the implementation of this methodology

Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems

Abstract: This paper presents the development of a particle filtering (PF) based method for fault detection and isolation (FDI) in stochastic nonlinear dynamic systems. The FDI problem is formulated in the multiple model (MM) environment, then by combining the likelihood ratio (LR) test with the PF, a new FDI scheme is developed. The simulation results on a highly nonlinear system are provided which demonstrate the effectiveness of the proposed method.

Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Abstract: Stimulated by the growing demand for improving the reliability and performance of systems, fault-tolerant control has been receiving significant attention since its goal is to detect the occurrence of faults and achieve satisfactory system performance in the presence of faults. To develop an intelligent fault-tolerant control system, we begin by constructing a design model of the system using a hierarchical learning structure in the form of Takagi-Sugeno fuzzy systems. Afterwards, the fault-tolerant control scheme is designed based on stable adaptive fuzzy/neural control, where its online learning capabilities are used to capture the unknown dynamics caused by faults. Finally, the effectiveness of the proposed methods has been studied by extensive analysis of system zero dynamics and asymptotic tracking abilities for both indirect and direct adaptive control cases, and by "component level model" simulation of the General Electric XTE46 turbine engine.

Design of fault diagnosis filters and fault-tolerant control for a class of nonlinear systems

Abstract: This paper presents a set of algorithms for fault diagnosis and fault tolerant control strategy for affine nonlinear systems subjected to an unknown time-varying fault vector. First, the design of fault diagnosis filter is performed using nonlinear observer techniques, where the system is decoupled through a nonlinear transformation and an observer is used to generate the required residual signal. By introducing an extra input to the observer, a direct estimation of the time-varying fault is obtained when the residual is controlled, by this extra input, to zero. The stability analysis of this observer is proved and some relevant sufficient conditions are obtained. Using the estimated fault vector, a fault tolerant controller is established which guarantees the stability of the closed loop system. The proposed algorithm is applied to a combined pH and consistency control system of a pilot paper machine, where simulations are performed to show the effectiveness of the proposed approach.

Detection and identification of faulty sensors in dynamic processes

Abstract: A novel method proposed detects and identifies faulty sensors in dynamic systems using a subspace identification model. A consistent estimate of this subspace model was obtained from noisy input and output measurements by using errors-in-variables subspace identification algorithms. A parity vector was generated, which was decoupled from the system state, leading to a model residual for fault detection. An exponentially weighted moving average (EWMA) filter was applied to the residual to reduce false alarms due to noise. To identify faulty sensors, a dynamic structured residual approach with maximized sensitivity is proposed which generates a set of structured residuals, each decoupled from one subset of faults but most sensitive to others. All the structured residuals are also subject to an EWMA filtering to reduce the noise effect. Confidence limits for filtered structured residuals were determined using statistical inferential techniques. Other indices like generalized likelihood ratio and cumulative variance were compared to identify different types of faulty sensors. The fault magnitude was then estimated based on the model and faulty data. Data from a simulated 4 × 4 process and an industrial waste-water reactor were used to test the effectiveness of this method, where four types of sensor faults, including bias, precision degradation, drift, and complete failure, were tested.

Pattern Recognition-A Technique for Induction Machines Rotor Broken Bar Detection

Abstract: A pattern recognition technique based on Bayes minimum error classifier is developed to detect broken rotor bar faults in induction motors at the steady state. The proposed algorithm uses only stator currents as input without the need for any other variables. Initially, rotor speed is estimated from the stator currents, then appropriate features are extracted. The produced feature vector is normalized and fed to the trained classifier to see if the motor is healthy or has broken bar faults. Only the number of poles and rotor slots are needed as pre-knowledge information. A theoretical approach together with experimental results derived from a 3 hp AC induction motor show the strength of the proposed method. In order to cover many different motor load conditions, data are obtained from 10% to 130% of the rated load for both a healthy induction motor and an induction motor with a rotor having 4 broken bars.

Framework for modeling software reliability, using various testing-efforts and fault-detection rates

Abstract: This paper proposes a new scheme for constructing software reliability growth models (SRGM) based on a nonhomogeneous Poisson process (NHPP). The main focus is to provide an efficient parametric decomposition method for software reliability modeling, which considers both testing efforts and fault detection rates (FDR). In general, the software fault detection/removal mechanisms depend on previously detected/removed faults and on how testing efforts are used. From practical field studies, it is likely that we can estimate the testing efforts consumption pattern and predict the trends of FDR. A set of time-variable, testing-effort-based FDR models were developed that have the inherent flexibility of capturing a wide range of possible fault detection trends: increasing, decreasing, and constant. This scheme has a flexible structure and can model a wide spectrum of software development environments, considering various testing efforts. The paper describes the FDR, which can be obtained from historical records of previous releases or other similar software projects, and incorporates the related testing activities into this new modeling approach. The applicability of our model and the related parametric decomposition methods are demonstrated through several real data sets from various software projects. The evaluation results show that the proposed framework to incorporate testing efforts and FDR for SRGM has a fairly accurate prediction capability and it depicts the real-life situation more faithfully. This technique can be applied to wide range of software systems.

Device and method for managing fault detection and fault isolation in voice and data networks

Abstract: The present invention provides a device and method for managing fault detection and isolation in voice and data networks. Specifically, the present invention is a device and method for fault detection and fault isolation that can be used to validate connectivity of a central site to a number of connected client devices. The present invention operates stage by stage, working from the central site back to a connected client device. The present invention can be controlled from a central maintenance station and as such does not require direct user enabling. In one embodiment, the present invention provides an intelligent device (e.g., an intelligent data concentrator) for coupling an electronic device to a network comprising a first interface for communicatively coupling the intelligent device to the network and a second interface for communicatively coupling the intelligent device to a plurality of client devices such that the client devices are communicatively coupled to the network. The intelligent device also comprises means for processing and interpreting data coupled to the first interface, and fault detection means coupled to the means for processing and interpreting data, wherein the fault detection means performs fault detection in the network. In one embodiment, the intelligent device operates in conjunction with a central control site in performing fault detection.

Automatic Extraction of Fault Surfaces from Three-Dimensional Seismic Data

Abstract: Interpretation of faults in seismic data is today a time consuming manual task. A new method for automatic extraction of fault surfaces from conditioned fault enhancing attributes is presented. The backbone of this process is formed by the attribute set chosen. Attributes well suited for fault detection and enhancement will be defined and procedures for fault surface extraction explained. A case study of automatically interpreted fault surfaces will be shown.

Diagnosis methods for electronic controlled vehicles.

Abstract: The development of automotive systems shows an increasing number of sensors, actuators and microelectronic controllers, partially for active driver assistance. However, electronic and electrical components have quite different failure behavior and in general lower reliability than mechanical components. On the other side microcomputers can also be used for fault detection and diagnosis. The contribution therefore shows how model-based fault detection and diagnosis methods together with few available measurements can be applied for automobiles. After an introduction, a short summary is given for fault detection and diagnosis methods, especially for model-based methods. Then some research results are shown, like the fault detection and diagnosis of an electromechanical throttle actuator, a suspension system and the lateral behavior of a passenger car. Finally, methods for fault-tolerant sensors and actuators are discussed which are required for drive-by-wire systems.

Finite Element Analysis of Internal Winding Faults in Distribution Transformers

Abstract: With the appearance of deregulation, distribution transformer predictive maintenance is becoming more important for utilities to prevent forced outages with the consequential costs. To detect and diagnose a transformer internal fault requires a transformer model to simulate these faults. This paper presents finite element analysis of internal winding faults in a distribution transformer. The transformer with a turn-to-earth fault or a turn-to-turn fault is modeled using coupled electromagnetic and structural finite elements. The terminal behaviors of the transformer are studied by an indirect coupling of the finite element method and circuit simulation. The procedure was realized using a commercially available software. The normal case and various faulty cases were simulated and the terminal behaviors of the transformer were studied and compared with field experimental results. The comparison results validate the finite element model to simulate internal faults in a distribution transformer.

An LMI approach to the design of fault detection filter for time-delay LTI systems with unknown inputs

Abstract: The robust fault detection problems for time-delay LTI systems with unknown inputs are studied. The basic idea of our study is to express the robustness as well as the sensitivity of residual signals to the unknown inputs as well as to the faults in terms of L/sub 2-/gain and, based on it, to formulate the design of fault detection filters as an optimization problem. Applying the H/sub /spl infin// optimization technique, an LMI approach to the solution of the optimization problem is then proposed. The main results consist of the derivation of the existence conditions of the fault detection filter for time-delay LTI systems and further the solution of the optimal design problem.

A wavelet-based approach to abrupt fault detection and diagnosis of sensors

Abstract: This paper describes a novel wavelet-based approach to the abrupt fault detection and diagnosis of sensors. By the use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multiscale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults. Synthetic data simulated by means of a computer using real-word data from a general-purpose pressure sensor have verified the effectiveness of the proposed method.

Wavelet transforms in power systems Part 2 Examples of application to actual power system transients

Abstract: This is the second in a series of two and illustrates some practical applications of the wavelet transform to power systems: protection/fault detection, detection of power quality disturbances and analysis of the partial discharge phenomenon in GIS (gas-insulated substations). Emphasis is placed on a number of practical issues.

A modeling method of a high impedance fault in a distribution system using two series time-varying resistances in EMTP

Abstract: More reliable algorithms for detecting a high impedance fault (HIF) require the voltage and current data at a relaying point instead of a faulted branch when HIF occurs. Thus, an accurate modeling method of HIF is essential for the development of a reliable detecting algorithm. The data should contain the complex characteristics of HIF such as buildup and shoulder as well as nonlinearity and asymmetry. This paper presents a modeling method of the above-mentioned HIF characteristics. Among the experiment data on a 22.9 (kV) distribution system conducted by Korea Electric Power Corporation (KEPCO), experiment data showing all the above characteristics is chosen in this paper. Two series time-varying resistances (TVRs) controlled by transient analysis of control systems (TACS) in EMTP are employed for modeling. One TVR is used for nonlinearity and asymmetry from the voltage-current characteristic for one cycle in the steady state after HIF, and then the other TVR for buildup and shoulder from the waveforms in the transient state after HIF. The comparison of the modeling results with the experiment data shows close correspondence. With the developed HIF model, the voltage and current at the relaying point are obtained with various load condition and fault condition such as fault distance and inception angle.

Fault Detection by Mining Association Rules from House-keeping Data

Abstract: This paper proposes a novel anomaly detection method for spacecraft systems based on data-mining techniques. This method automatically constructs a system behavior model in the form of a set of rules by applying pattern clustering and association rule mining to the time-series data obtained in the learning phase, then detects anomalies by checking the subsequent on-line data with the acquired rules. A major advantage of this approach is that it requires little a priori knowledge on the system.