Showing papers on "Fault detection and isolation published in 1999"

Test case prioritization: an empirical study

Abstract: Test case prioritization techniques schedule test cases for execution in an order that attempts to maximize some objective function. A variety of objective functions are applicable; one such function involves rate of fault detection-a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during regression testing can provide faster feedback on a system under regression test and let debuggers begin their work earlier than might otherwise be possible. In this paper we describe several techniques for prioritizing test cases and report our empirical results measuring the effectiveness of these techniques for improving rate of fault detection. The results provide insights into the tradeoffs among various techniques for test case prioritization.

Condition monitoring and fault diagnosis of electrical machines-a review

Abstract: Research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this possible. Primarily, these techniques depend upon locating specific harmonic components in the line current, also known as motor current signature analysis (MCSA). These harmonic components are usually different for different types of faults. However with multiple faults or different varieties of drive schemes, MCSA can become an onerous task as different types of faults and time harmonics may end up generating similar signatures. Thus other signals such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. Human involvement in the actual fault detection decision making is slowly being replaced by automated tools such as expert systems, neural networks, fuzzy logic based systems to name a few. Keeping in mind the need for future research, this review paper describes different types of faults and the signatures they generate and their diagnostics' schemes.

Observer-based approach to fault detection and isolation for nonlinear systems

Abstract: The design of a residual generator for fault detection and isolation (FDI) in nonlinear systems which are affine in the control signals and in the failure modes is studied, First, the problem statement used for linear systems is extended, and a set of sufficient conditions for the existence of a solution is given. Next, circumstances under which high-gain observers for uniformly observable systems can be used in the synthesis of the residual generator are provided.

Induction motors' faults detection and localization using stator current advanced signal processing techniques

Abstract: The knowledge about fault mode behavior of an induction motor drive system is extremely important from the standpoint of improved system design, protection, and fault-tolerant control. This paper addresses the application of motor current spectral analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. Intensive research effort has been for some time focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult since distortions of the current waveform caused by the abnormalities in the induction motor are usually minute. This paper takes the initial step to investigate the efficiency of current monitoring for diagnostic purposes. The effects of stator current spectrum are described and the related frequencies determined. In the present investigation, the frequency signature of some asymmetrical motor faults are well identified using advanced signal processing techniques, such as high-resolution spectral analysis. This technique leads to a better interpretation of the motor current spectra. In fact, experimental results clearly illustrate that stator current high-resolution spectral analysis is very sensitive to induction motor faults modifying main spectral components, such as voltage unbalance and single-phasing effects.

New directions in nonlinear observer design

Abstract: A viewpoint on observability and observer design for nonlinear systems.- Model-based observers for tire/road contact friction prediction.- Observer design for nonlinear oscillatory systems.- Transformation to state affine system and observer design.- On existence of extended observers for nonlinear discrete-time systems.- Stability analysis and observer design for nonlinear diffusion processes.- Nonlinear passive observer design for ships with adaptive wave filtering.- Nonlinear observer design for integration of DGPS and INS.- Variants of nonlinear normal form observer design.- Separation results for semiglobal stabilization of nonlinear systems via measurement feedback.- Observer-controller design for global tracking of nonholonomic systems.- A separation principle for a class of euler-lagrange systems.- High-gain observers in nonlinear feedback control.- Output-Feedback Control of stochastic nonlinear systems.- Output feedback control of food-chain systems.- Output feedback tracking control for ships.- Dynamic UCO controllers and semiglobal stabilization of uncertain nonminimum phase systems by output feedback.- Fault detection observer for a class of nonlinear systems.- Nonlinear observer for signal and parameter fault detection in ship propulsion control.- Nonlinear observers for fault detection and isolation.- Application of nonlinear observers to fault detection and isolation.- Innovation generation for bilinear systems with unknown inputs.- Synchronization through extended kalman filtering.- Nonlinear discrete-time observers for synchronization problems.- Chaos synchronization.

A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process

Abstract: Multivariate statistical process control (MSPC) tools have been developed for monitoring a Lam 9600 TCP metal etcher at Texas Instruments. These tools are used to determine if the etch process is operating normally or if a system fault has occurred. Application of these methods is complicated because the etch process data exhibit a large amount of normal systematic variation. Variations due to faults of process concern can be relatively minor in comparison. The Lam 9600 used in this study is equipped with several sensor systems including engineering variables (e.g. pressure, gas flow rates and power), spatially resolved optical emission spectroscopy (OES) of the plasma and a radio-frequency monitoring (RFM) system to monitor the power and phase relationships of the plasma generator. A variety of analysis methods and data preprocessing techniques have been tested for their sensitivity to specific system faults. These methods have been applied to data from each of the sensor systems separately and in combination. The performance of the methods on a set of benchmark fault detection problems is presented and the strengths and weaknesses of the methods are discussed, along with the relative advantages of each of the sensor systems. Copyright © 1999 John Wiley & Sons, Ltd.

Diagnosis of continuous valued systems in transient operating regions

Abstract: The complexity of present day embedded systems (continuous processes controlled by digital processors), and the increased demands on their reliability motivate the need for monitoring and fault isolation capabilities in the embedded processors. This paper develops monitoring, prediction, and fault isolation methods for abrupt faults in complex dynamic systems. The transient behavior in response to these faults is analyzed in a qualitative framework using parsimonious topological system models. Predicted transient effects of hypothesized faults are captured in the form of signatures that specify future faulty behavior as higher order time-derivatives. The dynamic effects of faults are analyzed by a progressive monitoring scheme till transient analysis mechanisms have to be suspended in favor of steady state analysis. This methodology has been successfully applied to monitoring of the secondary sodium cooling loop of a fast breeder reactor.

An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current

Abstract: It is well known that motor current is a nonstationary signal, the properties of which vary with respect to the time-varying normal operating conditions of the motor. As a result, Fourier analysis makes it difficult to recognize fault conditions from the normal operating conditions of the motor. Time-frequency analysis, on the other hand, unambiguously represents the motor current which makes signal properties related to fault detection more evident in the transform domain. In this paper, the authors present an adaptive, statistical, time-frequency method for the detection of broken bars and bearing faults. Due to the time-varying normal operating conditions of the motor and the effect of motor geometry on the current, they employ a training-based approach in which the algorithm is trained to recognize the normal operating modes of the motor before the actual testing starts. During the training stage, features which are relevant to fault detection are estimated using the torque and mechanical speed estimation. These features are then statistically analyzed and segmented into normal operating modes of the motor. For each mode, a representative and a threshold are computed and stored in a database to be used as a baseline during the testing stage. In the testing stage, the distance of the test features to the mode representatives are computed and compared with the thresholds. If it is larger than all the thresholds, the measurement is tagged as a potential fault signal. In the postprocessing stage, the testing is repeated for multiple measurements to improve the accuracy of the detection. The experimental results from their study suggest that the proposed method provides a powerful and a general approach to the motor-current-based fault detection.

Design and evaluation of system-level checks for on-line control flow error detection

Abstract: This paper evaluates the concurrent error detection capabilities of system-level checks, using fault and error injection. The checks comprise application and system level mechanisms to detect control flow errors. We propose Enhanced Control-Flow Checking Using Assertions (ECCA). In ECCA, branch-free intervals (BFI) in a given high or intermediate level program are identified and the entry and exit points of the intervals are determined. BFls are then grouped into blocks, the size of which is determined through a performance/overhead analysis. The blocks are then fortified with preinserted assertions. For the high level ECCA, we describe an implementation of ECCA through a preprocessor that will automatically insert the necessary assertions into the program. Then, we describe the intermediate implementation possible through modifications made on gee to make it ECCA capable. The fault detection capabilities of the checks are evaluated both analytically and experimentally. Fault injection experiments are conducted using FERRARI to determine the fault coverage of the proposed techniques.

The development of vehicle stability control at Ford

Abstract: Addresses realistic subjects encountered in the challenge of achieving technology improvement in a vehicle stability control system. They include driver intent recognition, vehicle status measurement and estimation, control target generation, system actuation efficiency and smoothness, road bank angle detection, system development and evaluation, and fault detection.

IBM S/390 parallel enterprise server G5 fault tolerance: a historical perspective

Abstract: Fault tolerance in IBM S/390® systems during the 1980s and 1990s had three distinct phases, each characterized by a different uptime improvement rate. Early TCM-technology mainframes delivered excellent data integrity, instantaneous error detection, and positive fault isolation, but had limited on-line repair. Later TCM mainframes introduced capabilities for providing a high degree of transparent recovery, failure masking, and on-line repair. New challenges accompanied the introduction of CMOS technology. A significant reduction in parts count greatly improved intrinsic failure rates, but dense packaging disallowed on-line CPU repair. In addition, characteristics of the microprocessor technology posed difficulties for traditional in-line error checking. As a result, system fault-tolerant design, particularly in CPUs and memory, underwent another evolution from G1 to G5. G5 implements an innovative design for a high-performance, fault-tolerant single-chip microprocessor. Dynamic CPU sparing delivers a transparent concurrent repair mechanism. A new internal channel provides a high-performance, highly available Parallel Sysplex® in a single mainframe. G5 is both the culmination of decades of innovation and careful implementation, and the highest achievement of S/390 fault-tolerant design.

A fault detection service for wide area distributed computations

Abstract: The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to trade off timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver.

Closed loop control impact on the diagnosis of induction motors faults

Abstract: In this paper, the impact of control on faulted induction machine behavior is presented. The diagnostic indexes usually used for open-loop operation are no more effective. Simulation and experimental results show that the spectrum of the field current component i/sub d/ in a field-oriented controlled machine has suitable features that can lead to an effective diagnostic procedure for closed loop induction machines. Specifically in case of stator and rotor faults, the spectrum components at frequencies respectively -f and 2sf are quite independent of control parameters and dependent on the fault extent.

Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis

Abstract: Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, namely, fuzzy adaptive learning control/decision network (FALCON) and adaptive network based fuzzy inference system (ANFIS), with applications to induction motor fault detection/diagnosis problems. The general specifications of the NN/FZ systems are discussed. In addition, the fault detection/diagnosis structures are analyzed and compared with regard to their learning algorithms, initial knowledge requirements, extracted knowledge types, domain partitioning, rule structuring and modifications. Simulated experimental results are presented in terms of motor fault detection accuracy and knowledge extraction feasibility. Results suggest new and promising research areas for using NN/FZ inference systems for incipient fault detection and diagnosis in induction motors.

Soft-error detection through software fault-tolerance techniques

Abstract: The paper describes a systematic approach for automatically introducing data and code redundancy into an existing program written using a high-level language. The transformations aim at making the program able to detect most of the soft-errors affecting data and code, independently of the Error Detection Mechanisms (EDMs) possibly implemented by the hardware. Since the transformations can be automatically applied as a pre-compilation phase, the programmer is freed from the cost and responsibility of introducing suitable EDMs in its code. Preliminary experimental results are reported, showing the fault coverage obtained by the method, as well as some figures concerning the slow-down and code size increase it causes.

Locating sensors in complex chemical plants based on fault diagnostic observability criteria

Abstract: Fault diagnosis is an important task for the safe and optimal operation of chemical processes. Hence, this area has attracted considerable attention from researchers in the past few years. A variety of approaches have been proposed for solving this problem. All approaches for fault detection and diagnosis in some sense involve the comparison of the observed hehavior of the process to a reference model. The process behavior is inferred using sensors measuring the important variables in the process. Hence, the efficiency of the diagnostic approach depends critically on the location of sensors monitoring the process variables. The emphasis of most of the work on fault diagnosis has been more on procedures to perform diagnosis given a set of sensors and less on the actual location of sensors for efficient identification of faults. A digraph-based approach is proposed for the problem of sensor location for identification of faults. Various graph algorithms that use the developed digraph in deciding the location of sensors based on the concepts of observability and resolution are discussed. Simple examples are provided to explain the algorithms, and a complex FCCU case study is also discussed to underscore the utility of the algorithm for large flow sheets. The significance and scope of the proposed algorithms are highlighted.

A time/structure based software reliability model

Abstract: The past 20 years have seen the formulation of numerous analytical software reliability models for estimating the reliability growth of a software product. The predictions obtained by applying these models tend to be optimistic due to the inaccuracies in the operational profile, and saturation effect of testing. Incorporating knowledge gained about some structural attribute of the code, such as test coverage, into the timeddomain models can help alleviate this optimistic trend. In this paper we present an enhanced nondhomogeneous Poisson process (ENHPP) model which incorporates explicitly the timedvarying testdcoverage function in its analytical formulation, and provides for defective fault detection and test coverage during the testing and operational phases. It also allows for a time varying fault detection rate. The ENHPP model offers a unifying framework for all the previously reported finite failure NHPP models via test coverage. We also propose the logdlogistic coverage function which can capture an increasing/decreasing failure detection rate per fault, which cannot be accounted for by the previously reported finite failure NHPP models. We present a methodology based on the ENHPP model for reliability prediction earlier in the testing phase. Expressions for predictions in the operational phase of the software, software availability, and optimal software release times subject to various constraints such as cost, reliability, and availability are developed based on the ENHPP model. We also validate the ENHPP model based on four different coverage functions using five failure data sets.

Integrated control and fault detection of HVAC equipment

Abstract: Fault detection is implemented on a finite state machine controller for an air handling system. The method employs data, regarding the system performance in the current state and upon a transition occurring, to determine whether a fault condition exists. The fault detection may be based on saturation of the system control or on a comparison of actual performance to a mathematical model of the air handling system. As a consequence, the control does not have to be in steady-state operation to perform fault detection.

Fault tolerant architecture for a personal vehicle

Abstract: A motorized vehicle capable of fault detection and of operation after a fault has been detected. The vehicle has a plurality of control components coupled to a motorized drive and a comparator for comparing the output of each of the control components with outputs of other control components so that failures may be identified. The vehicle may have multiple processors coupled to a plurality of control channels by means of a bus and a decision arrangement that suppresses the output of any processor for which a failure has been identified.

Moving horizon estimation for hybrid systems and fault detection

Abstract: An approach for fault detection and state estimation of hybrid systems is presented. The method relies on the modeling framework for hybrid systems introduced by Bemporad and Morari (1999). This framework considers interacting propositional logic, automata, continuous dynamics and constraints. The proposed approach is illustrated by considering the fault detection problem of the three-tank benchmark system.

Fail safe fault interrupter

Abstract: An electronic test circuit for the self-testing of fault detection devices such as GFCI's, AFCI's and RCD's enhances the safety of such devices by automatically testing the function of all components of these detection components without the need for manual intervention. This self-test device tests the functioning of the primary circuit breaker and detects failure modes such as welded contacts in the circuit breaker. By using a secondary, “one-shot” circuit breaker, power may be safely and automatically removed from a malfunctioning fault detection device.

Norm based design of fault detectors

Abstract: The design of fault detectors for fault detection and isolation (FDI) in dynamic systems is considered in this paper from a norm based point of view. An analysis of norm based threshold selection is given based on different formulations of FDI problems. Both the nominal FDI problem as well as the uncertain FDI problem will be considered. With reference to this analysis, a performance index based on norms of the involved transfer functions is given. A method for designing FDI filters which will minimize the performance index is also given.

COFTA: hardware-software co-synthesis of heterogeneous distributed embedded systems for low overhead fault tolerance

Abstract: Embedded systems employed in critical applications demand high reliability and availability in addition to high performance. Hardware-software co-synthesis of an embedded system is the process of partitioning, mapping, and scheduling its specification into hardware and software modules to meet performance, cost, reliability, and availability goals. In this paper, we address the problem of hardware-software co-synthesis of fault-tolerant real-time heterogeneous distributed embedded systems. Fault detection capability is imparted to the embedded system by adding assertion and duplicate-and-compare tasks to the task graph specification prior to co-synthesis. The dependability (reliability and availability) of the architecture is evaluated during co-synthesis. Our algorithm, called COFTA (Co-synthesis Of Fault-Tolerant Architectures), allows the user to specify multiple types of assertions for each task. It uses the assertion or combination of assertions which achieves the required fault coverage without incurring too much overhead. We propose new methods to: 1) Perform fault tolerance based task clustering, which determines the best placement of assertion and duplicate-and-compare tasks, 2) Derive the best error recovery topology using a small number of extra processing elements, 3) Exploit multidimensional assertions, and 4) Share assertions to reduce the fault tolerance overhead. Our algorithm can tackle multirate systems commonly found in multimedia applications. Application of the proposed algorithm to a large number of real-life telecom transport system examples (the largest example consisting of 2,172 tasks) shows its efficacy. For fault secure architectures, which just have fault detection capabilities, COFTA is able to achieve up to 48.8 percent and 25.6 percent savings in embedded system cost over architectures employing duplication and task-based fault tolerance techniques, respectively. The average cost overhead of COFTA fault-secure architectures over simplex architectures is only 7.3 percent. In case of fault-tolerant architectures, which cannot only detect but also tolerate faults, COFTA is able to achieve up to 63.1 percent and 23.8 percent savings in embedded system cost over architectures employing triple-modular redundancy, and task-based fault tolerance techniques, respectively. The average cost overhead of COFTA fault-tolerant architectures over simplex architectures is only 55.4 percent.

Fault diagnosis of electrical machines-a review

Abstract: Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this possible. Primarily, these techniques depend upon locating specific harmonic components in the line current, also known as motor current signal analysis (MCSA). These harmonic components are usually different for different types of faults. However with multiple faults or different varieties of drive schemes, MCSA can become an onerous task as different types of faults and time harmonics may end up generating similar signatures. Thus other signals such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing the different types of fault and the signatures they generate and their diagnostics schemes, will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye-view to a new researcher in this area.

Sensor Fault Detection via Multiscale Analysis and Dynamic PCA

Abstract: In this paper, a new approach to sensor validation in real time is described that is based on (1) representation of the sensor signal by wavelets, (2) decomposition of the signal for different freq...