Showing papers on "Fault detection and isolation published in 2005"
TL;DR: A review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.
Abstract: Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), 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. In addition, 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. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults 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.
1,869 citations
13 Mar 2005
TL;DR: Simulation results indicate that these algorithms can clearly detect the event boundary and can identify faulty sensors with a high accuracy and a low false alarm rate when as many as 20% sensors become faulty.
Abstract: This paper targets the identification of faulty sensors and detection of the reach of events in sensor networks with faulty sensors. Typical applications include the detection of the transportation front line of a contamination and the diagnosis of network health. We propose and analyze two novel algorithms for faulty sensor identification and fault-tolerant event boundary detection. These algorithms are purely localized and thus scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify faulty sensors with a high accuracy and a low false alarm rate when as many as 20% sensors become faulty. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs, a dramatic improvement over existing works that take only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as the "events" can be modelled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.
422 citations
TL;DR: A new fault detection and identification method based on kernel principal component analysis (PCA) that uses kernel functions, which is a challenging problem in nonlinear PCA, is formulated based on a robust reconstruction error calculation.
417 citations
TL;DR: In this article, an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes is presented.
Abstract: Multivariate monitoring and control schemes based on latent variable methods have been receiving increasing attention by industrial practitioners in the last 15 years. Several companies have enthusiastically adopted the methods and have reported many success stories. Applications have been reported where multivariate statistical process control, fault detection and diagnosis is achieved by utilizing the latent variable space, for continuous and batch processes, as well as, for process transitions as for example start ups and re-starts. This paper gives an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes. It provides a critical review of the methodology and describes how it is transferred to the industrial environment. Recent applications of latent variable methods to process control as well as to image analysis for monitoring and feedback control are discussed. Finally it is emphasized that the multivariate nature of the data should be preserved when data compression and data preprocessing is applied. It is shown that univariate data compression and reconstruction may hinder the validity of multivariate analysis by introducing spurious correlations. Copyright © 2005 John Wiley & Sons, Ltd.
335 citations
18 Apr 2005
TL;DR: The idea is to handle a collision at a generic point along the robot as a fault of its actuating system as well as a previously developed dynamic FDI technique, which does not require acceleration or force measurements.
Abstract: We consider the problem of real-time detection of collisions between a robot manipulator and obstacles of unknown geometry and location in the environment without the use of extra sensors. The idea is to handle a collision at a generic point along the robot as a fault of its actuating system. A previously developed dynamic FDI (fault detection and isolation) technique is used, which does not require acceleration or force measurements. The actual robot link that has collided can also be identified. Once contact has been detected, it is possible to switch to a suitably defined hybrid force/motion controller that enables to keep the contact, while sliding on the obstacle, and to regulate the interaction force. Simulation results are shown for a two-link planar robot.
332 citations
12 Dec 2005
TL;DR: This paper proves that the set of all possible firing sequences corresponding to a given observation can be described as follows, and proposes a simple tabular algorithm to determine a basis reachability tree that can be used as a diagnoser.
Abstract: In this paper we present an efficient approach for the fault detection of discrete event systems using Petri nets. We assume that some of the transitions of the net are unobservable, including all those transitions that model faulty behaviors. We prove that the set of all possible firing sequences corresponding to a given observation can be described as follows. First a set of basis markings corresponding to the observation are computed together with the minimal set of transitions firings that justify them. Any other marking consistent with the observation must be reachable from a basis marking by firing only unobservable transitions. For the computation of the set of basis markings we propose a simple tabular algorithm and use it to determine a basis reachability tree that can be used as a diagnoser.
312 citations
TL;DR: The lowest level of sensitivity of system outputs to system inputs is defined as an H"- index, defined in terms of matrix equalities and inequalities, as a dual of the Bounded Real Lemma.
298 citations
26 Sep 2005
TL;DR: Past work in the data driven approach to prognosis is surveyed in data -driven fault detection and diagnosis, and in model -based diagnosis and prognosis, particularly as applied to space systems.
Abstract: *Integrated Systems Health Management includes fault detection, fault diagnosis (or fault isolation), and fault prognosis. We define prognosis to be detecting the precursors of a failure, and predicting how much time remains before a likely failure. Algorithms that use the data -driven approach to prognosis learn models directly from the data, rather than u sing a hand -built model based o n human experti se. This paper surveys past work in the data driven approach to prognosis. It also includes related work in data -driven fault detection and diagnosis, and in model -based diagnosis and prognosis, particularly as applied to space systems.
282 citations
TL;DR: A new process monitoring method is proposed that is composed of a preanalysis step that first roughly identifies various clusters in a historical data set and then precisely isolates normal and abnormal data clusters by the k-means clustering method and a fault diagnosis method based on fault directions in pairwise FDA.
Abstract: Multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) have been widely applied to the statistical process monitoring (SPM) of chemical processes and their effectiveness for fault detection is well recognized. These methods make use of normal process data to define a tight normal operation region for monitoring. In practice, however, historical process data are often corrupted with faulty data. In this paper, a new process monitoring method is proposed that is composed of three parts: (1) a preanalysis step that first roughly identifies various clusters in a historical data set and then precisely isolates normal and abnormal data clusters by the k-means clustering method; (2) a fault visualization step that visualizes high-dimensional data in 2-D space by performing global Fisher discriminant analysis (FDA), and (3) a new fault diagnosis method based on fault directions in pairwise FDA. A simulation example is used to demonstrate the performance of the proposed fault diagnosis method. An industrial film process is used to illustrate a realistic scenario for data preanalysis, fault visualization, and fault diagnosis. In both examples, the contribution plots method, based on fault directions in pairwise FDA, shows superior capability for fault diagnosis to the contribution plots method based on PCA. © 2005 American Institute of Chemical Engineers AIChE J, 51: 555–571, 2005
238 citations
TL;DR: In this paper, the authors studied the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor using a sensor-based technique using the mains current measurement.
Abstract: The aim of this paper is to study the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. The proposed approach is a sensor-based technique using the mains current measurement. A localization domain made with seven patterns is built with the stator Concordia mean current vector. One is dedicated to the healthy domain and the last six are to each inverter switch. A probabilistic approach for the definition of the boundaries increases the robustness of the method against the uncertainties due to measurements and to the PWM. In high-power equipment where it is crucial to detect and diagnose the inverter faulty switch, a simple algorithm compares the patterns and generates a Boolean indicating the faulty device. In low-power applications (less than 1 kW) where only fault detection is required, a radial basis function (RBF) evolving architecture neural network is used to build the healthy operation area. Simulated experimental results on 0.3- and 1.5-kW induction motor drives show the feasibility of the proposed approach.
228 citations
TL;DR: A new high-resolution reflectometry technique that operates simultaneously in both the time and frequency domains, which rests upon time-frequency signal analysis and utilizes a chirp signal multiplied by a Gaussian time envelope.
Abstract: In this paper, we introduce a new high-resolution reflectometry technique that operates simultaneously in both the time and frequency domains. The approach rests upon time-frequency signal analysis and utilizes a chirp signal multiplied by a Gaussian time envelope. The Gaussian envelope provides time localization, while the chirp allows one to excite the system under test with a swept sinewave covering a frequency band of interest. This latter capability is of particular interest when testing communication cables and systems. Sensitivity in detecting the reflected signal is provided by a time-frequency cross-correlation function. The approach is verified by experimentally locating various types of faults, located at various distances, in RG 142 and RG 400 coaxial cables.
TL;DR: Research on execution monitoring in its own is still not very common within the field of robotics and autonomous systems, but it is more common that researchers interested in control architectures or e-commerce systems focus on this area.
TL;DR: Examination of the performance of the proposed method for machine fault detection and classification in electro-mechanical machinery from vibration measurements using one-class support vector machines (SVMs) is examined by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.
TL;DR: A two-step design procedure is proposed to derive non-conservative robust FDI filters for LTI uncertain systems under feedback control and the solution is based on the generalized structured singular value.
08 Jul 2005
TL;DR: In this article, the robust fault detection problem for a class of discrete-time linear Markovian jump systems with an unknown input is formulated as an H∞-filtering problem, in which the filter matrices are dependent on the system mode.
Abstract: The paper deals with the robust fault detection problem for a class of discrete-time linear Markovian jump systems with an unknown input. By using a general observer-based fault detection filter as residual generator, the robust fault detection filter design is formulated as an H∞-filtering problem, in which the filter matrices are dependent on the system mode, i.e. the residual generator is a Markovian jump linear system as well. The main objective is to make the error between residual and fault (or, more generally, weighted fault) as small as possible. A sufficient condition to solve this problem is established in terms of the feasibility of certain linear matrix inequalities (LMI), which can be solved with the aid of Matlab LMI Toolbox. A numerical example is given to illustrate the effectiveness of the proposed techniques.
Patent•
05 Apr 2005TL;DR: In this article, an apparatus and method for performing automated testing and trouble isolation of a communications link in an access network is described, without taking the communications link out of service for the duration of the test.
Abstract: An apparatus and method for performing automated testing and trouble isolation of a communications link in an access network is described. Communications link testing may occur without taking the communications link out of service for the duration of the test.
01 Jan 2005
TL;DR: In this article, a performance evaluation for the inverter connected to the machine with variable stator voltage and frequency is presented, showing the influence of the applied standard field oriented control on the currents during a fault.
Abstract: Variable speed drives have become industrial standard in many applications Therefore fault diagnosis of voltage source inverters is becoming more and more important One possible fault within the inverter is an open circuit transistor fault An overview of the different strategies to detect this fault is given, including the algorithms used to localize the open transistor Previous work showed significant differences among the available methods to detect such a fault for a mains side active rectifier This paper extends the performance evaluation for the inverter connected to the machine with variable stator voltage and frequency Simulation results are presented They show the influence of the applied standard field oriented control on the currents during a fault An experimental setup in the laboratory is used to validate simulation results Typical detection results are presented including time-to-detection measurements Robust detection of open transistor faults has been found to be possible
TL;DR: In this article, an online strategy was developed to detect, diagnose and validate sensor faults in centrifugal chillers based on principal component analysis (PCA) and the Q-statistic and Q-contribution plots were used to detect and diagnose the sensor faults.
TL;DR: This paper presents a new fault detection and diagnosis (FDD) algorithm for general stochastic systems that uses the measured output probability density functions and the input of the system to construct a stable filter-based residual generator such that the fault can be detected and diagnosed.
Abstract: This paper presents a new fault detection and diagnosis (FDD) algorithm for general stochastic systems Different from the classical FDD design, the distribution of system output is supposed to be measured rather than the output signal itself The task of such an FDD algorithm design is to use the measured output probability density functions (PDFs) and the input of the system to construct a stable filter-based residual generator such that the fault can be detected and diagnosed For this purpose, square root B-spline expansions are applied to model the output PDFs and the concerned problem is transformed into a nonlinear FDD algorithm design subjected to a nonlinear weight dynamical system A linear matrix inequality based solution is presented such that the estimation error system is stable and the fault can be detected through a threshold Moreover, an adaptive fault diagnosis method is also provided to estimate the size of the fault Simulations are provided to show the efficiency of the proposed approach
TL;DR: In this article, a fault detection and identification approach based on a multiblock partial least squares (MBPLS) method to monitor a complex chemical process and to model a key process quality variable simultaneously is discussed.
TL;DR: In this article, the authors present a systematic development of fault detection and diagnosis methods for two system components of Diesel engines, the intake system and the injection system together with the combustion process.
TL;DR: ProFiT is introduced, a technique which adjusts the level of protection and performance at fine granularities through software control when coupled with software-controllable techniques like SWIFT and CRAFT, which offers attractive and novel reliability options.
Abstract: Traditional fault-tolerance techniques typically utilize resources ineffectively because they cannot adapt to the changing reliability and performance demands of a system. This paper proposes software-controlled fault tolerance, a concept allowing designers and users to tailor their performance and reliability for each situation. Several software-controllable fault-detection techniques are then presented: SWIFT, a software-only technique, and CRAFT, a suite of hybrid hardware/software techniques. Finally, the paper introduces PROFiT, a technique which adjusts the level of protection and performance at fine granularities through software control. When coupled with software-controllable techniques like SWIFT and CRAFT, PROFiT offers attractive and novel reliability options.
01 May 2005
TL;DR: This paper identifies hybrid hardware/software fault-detection mechanisms as promising alternatives to hardware-only and software-only systems and proposes and evaluates CRAFT, a suite of three such hybrid techniques, to illustrate the potential of the hybrid approach.
Abstract: As chip densities and clock rates increase, processors are becoming more susceptible to transient faults that can affect program correctness. Up to now, system designers have primarily considered hardware-only and software-only fault-detection mechanisms to identify and mitigate the deleterious effects of transient faults. These two fault-detection systems, however, are extremes in the design space, representing sharp trade-offs between hardware cost, reliability, and performance. In this paper, we identify hybrid hardware/software fault-detection mechanisms as promising alternatives to hardware-only and software-only systems. These hybrid systems offer designers more options to fit their reliability needs within their hardware and performance budgets. We propose and evaluate CRAFT, a suite of three such hybrid techniques, to illustrate the potential of the hybrid approach. For fair, quantitative comparisons among hardware, software, and hybrid systems, we introduce a new metric, Mean Work To Failure, which is able to compare systems for which machine instructions do not represent a constant unit of work. Additionally, we present a new simulation framework which rapidly assesses reliability and does not depend on manual identification of failure modes. Our evaluation illustrates that CRAFT, and hybrid techniques in general, offer attractive options in the fault-detection design space.
TL;DR: Based on the class of nonlinear systems and sensor bias faults under consideration, the stability and learning properties of the fault isolation estimators are obtained, adaptive thresholds are derived for the isolation estimator, and fault isolability conditions are rigorously investigated, characterizing the classof nonlinear faults that are isolable by the proposed scheme.
Abstract: This note presents a robust fault isolation scheme for a class of nonlinear systems with sensor bias type of faults. The proposed fault diagnosis architecture consists of a fault detection estimator and a bank of isolation estimators, each corresponding to a particular output sensor. Based on the class of nonlinear systems and sensor bias faults under consideration, the stability and learning properties of the fault isolation estimators are obtained, adaptive thresholds are derived for the isolation estimators, and fault isolability conditions are rigorously investigated, characterizing the class of nonlinear faults that are isolable by the proposed scheme. A simulation example is used to illustrate the effectiveness of the sensor bias fault isolation methodology.
TL;DR: Genetic programming is used to detect faults in rotating machinery to examine the performance of two-class normal/fault recognition and the results are compared with a few other methods for fault detection.
01 Jan 2005
TL;DR: A system-level fault detection and diagnostic method for heating, ventilation, and air conditioning (HVAC) systems was developed that functions as an interface between multiple, equipment-specific FDD tools and a human operator.
Abstract: A system-level fault detection and diagnostic (FDD) method for heating, ventilation, and air conditioning (HVAC) systems was developed. It functions as an interface between multiple, equipment-specific FDD tools and a human operator. The method resolves conflicting fault reports from equipment-specific FDD tools, performs FDD at the system level, and presents an integrated view of an HVAC system's fault status to an operator. A simulation study to test and evaluate the method was conducted.
TL;DR: In this paper, the authors presented an application of H ∞ fault detection and isolation (FDI) to the longitudinal motion of a Boeing 747-100/200 aircraft to detect and isolate faults in the elevator actuator and pitch rate sensor while attenuating the effect of disturbances and noise on the fault signals.
TL;DR: In this paper, the authors present the results of an experimental study of the detection of mechanical faults in an induction motor by means of analysis of combinations of permeance and magneto-motive force (MMF) harmonics.
Abstract: This paper presents the results of an experimental study of the detection of mechanical faults in an induction motor. As is reasonably well known, by means of analysis of combinations of permeance and magneto-motive force (MMF) harmonics, it is possible to predict the frequency of air gap flux density harmonics which occur as a result of certain irregularities in an induction motor. In turn, analysis of flux density harmonics allows the prediction of induced voltages and currents in the stator windings. Reviewing this theory, equations which may aid in the identification of mechanical faults are presented. These equations include both those which indicate eccentric conditions and those which have been suggested to help identify bearing faults. The development of test facility to create eccentricity faults and bearing fault conditions is described. This test facility allows rapid access to the motor bearings, allowing an investigation into the ability to detect faulted bearing conditions using stator current monitoring. Experimental test results are presented, indicating that it may be possible to detect bearing degradation using relatively simple and inexpensive equipment.
TL;DR: This analysis of the relationships between variable and literal faults, and among literal, operator, term, and expression faults, produces a richer set of findings that interpret previous empirical results, can be applied to the design and evaluation of test methods, and inform the way that test cases should be prioritized for earlier detection of faults.
Abstract: Kuhn, followed by Tsuchiya and Kikuno, have developed a hierarchy of relationships among several common types of faults (such as variable and expression faults) for specification-based testing by studying the corresponding fault detection conditions. Their analytical results can help explain the relative effectiveness of various fault-based testing techniques previously proposed in the literature. This article extends and complements their studies by analyzing the relationships between variable and literal faults, and among literal, operator, term, and expression faults. Our analysis is more comprehensive and produces a richer set of findings that interpret previous empirical results, can be applied to the design and evaluation of test methods, and inform the way that test cases should be prioritized for earlier detection of faults. Although this work originated from the detection of faults related to specifications, our results are equally applicable to program-based predicate testing that involves logic expressions.
TL;DR: The problem of achieving fault-tolerant supervision of discrete-event systems is considered from the viewpoint of safe and timely diagnosis of unobservable faults, and the new property of safe diagnosability is introduced and studied.