Showing papers on "Fault detection and isolation published in 2006"

Fault-Diagnosis Systems

Abstract: Fundamentals.- Supervision and fault management of processes - tasks and terminology.- Reliability, Availability and Maintainability (RAM).- Safety, Dependability and System Integrity.- Fault-Detection Methods.- Process Models and Fault Modelling.- Signal models.- Fault detection with limit checking.- Fault detection with signal models.- Fault detection with process-identification methods.- Fault detection with parity equations.- Fault detection with state observers and state estimation.- Fault detection of control loops.- Fault detection with Principal Component Analysis (PCA).- Comparison and combination of fault-detection methods.- Fault-Diagnosis Methods.- Diagnosis procedures and problems.- Fault diagnosis with classification methods.- Fault diagnosis with inference methods.- Fault-Tolerant Systems.- Fault-tolerant design.- Fault-tolerant components and control.- Application Examples.- Fault detection and diagnosis of DC motor drives.- Fault detection and diagnosis of a centrifugal pump-pipe-system.- Fault detection and diagnosis of an automotive suspension and the tire pressures.

Intelligent Fault Diagnosis and Prognosis for Engineering Systems

Abstract: PREFACE. ACKNOWLEDGMENTS. PROLOGUE. 1 INTRODUCTION. 1.1 Historical Perspective. 1.2 Diagnostic and Prognostic System Requirements. 1.3 Designing in Fault Diagnostic and Prognostic Systems. 1.4 Diagnostic and Prognostic Functional Layers. 1.5 Preface to Book Chapters. 1.6 References. 2 SYSTEMS APPROACH TO CBM/PHM. 2.1 Introduction. 2.2 Trade Studies. 2.3 Failure Modes and Effects Criticality Analysis (FMECA). 2.4 System CBM Test-Plan Design. 2.5 Performance Assessment. 2.6 CBM/PHM Impact on Maintenance and Operations: Case Studies. 2.7 CBM/PHM in Control and Contingency Management. 2.8 References. 3 SENSORS AND SENSING STRATEGIES. 3.1 Introduction. 3.2 Sensors. 3.3 Sensor Placement. 3.4 Wireless Sensor Networks. 3.5 Smart Sensors. 3.6 References. 4 SIGNAL PROCESSING AND DATABASE MANAGEMENT SYSTEMS. 4.1 Introduction. 4.2 Signal Processing in CBM/PHM. 4.3 Signal Preprocessing. 4.4 Signal Processing. 4.5 Vibration Monitoring and Data Analysis. 4.6 Real-Time Image Feature Extraction and Defect/Fault Classification. 4.7 The Virtual Sensor. 4.8 Fusion or Integration Technologies. 4.9 Usage-Pattern Tracking. 4.10 Database Management Methods. 4.11 References. 5 FAULT DIAGNOSIS. 5.1 Introduction. 5.2 The Diagnostic Framework. 5.3 Historical Data Diagnostic Methods. 5.4 Data-Driven Fault Classification and Decision Making. 5.5 Dynamic Systems Modeling. 5.6 Physical Model-Based Methods. 5.7 Model-Based Reasoning. 5.8 Case-Based Reasoning (CBR). 5.9 Other Methods for Fault Diagnosis. 5.10 A Diagnostic Framework for Electrical/Electronic Systems. 5.11 Case Study: Vibration-Based Fault Detection and Diagnosis for Engine Bearings. 5.12 References. 6 FAULT PROGNOSIS. 6.1 Introduction. 6.2 Model-Based Prognosis Techniques. 6.3 Probability-Based Prognosis Techniques. 6.4 Data-Driven Prediction Techniques. 6.5 Case Studies. 6.6 References. 7 FAULT DIAGNOSIS AND PROGNOSIS PERFORMANCE METRICS. 7.1 Introduction. 7.2 CBM/PHM Requirements Definition. 7.3 Feature-Evaluation Metrics. 7.4 Fault Diagnosis Performance Metrics. 7.5 Prognosis Performance Metrics. 7.6 Diagnosis and Prognosis Effectiveness Metrics. 7.7 Complexity/Cost-Benefit Analysis of CBM/PHM Systems. 7.8 References. 8 LOGISTICS: SUPPORT OF THE SYSTEM IN OPERATION. 8.1 Introduction. 8.2 Product-Support Architecture, Knowledge Base, and Methods for CBM. 8.3 Product Support without CBM. 8.4 Product Support with CBM. 8.5 Maintenance Scheduling Strategies. 8.6 A Simple Example. 8.7 References. APPENDIX. INDEX.

New positive-sequence voltage detector for grid synchronization of power converters under faulty grid conditions

Abstract: This paper deals with a fundamental aspect in the control of grid-connected power converters, i.e., the detection of the positive-sequence component at fundamental frequency of the utility voltage under unbalanced and distorted conditions. Accurate and fast detection of this voltage component under grid faults is essential to keep the control over the power exchange with the grid avoiding to trip the converter protections and allowing the ride-through of the transient fault. In this paper, the systematic use of well known techniques conducts to a new positive-sequence voltage detection system which exhibits a fast, precise, and frequency-adaptive response under faulty grid conditions. Three fundamental functional blocks make up the proposed detector, these are: i) the quadrature-signals generator (QSG), ii) the positive-sequence calculator (PSC), and iii) the phase-locked loop (PLL). A key innovation of the proposed system is the use of a dual second order generalized integrator (DSOGI) to implement the QSG. For this reason, the proposed positive-sequence detector is called DSOGI-PLL. A detailed study of the DSOGI-PLL and verification by simulation are performed in this paper. From the obtained results, it can be concluded that the DSOGI-PLL is a very suitable technique for characterizing the positive-sequence voltage under grid faults.

Using Mutation Analysis for Assessing and Comparing Testing Coverage Criteria

Abstract: The empirical assessment of test techniques plays an important role in software testing research. One common practice is to seed faults in subject software, either manually or by using a program that generates all possible mutants based on a set of mutation operators. The latter allows the systematic, repeatable seeding of large numbers of faults, thus facilitating the statistical analysis of fault detection effectiveness of test suites; however, we do not know whether empirical results obtained this way lead to valid, representative conclusions. Focusing on four common control and data flow criteria (block, decision, C-use, and P-use), this paper investigates this important issue based on a middle size industrial program with a comprehensive pool of test cases and known faults. Based on the data available thus far, the results are very consistent across the investigated criteria as they show that the use of mutation operators is yielding trustworthy results: generated mutants can be used to predict the detection effectiveness of real faults. Applying such a mutation analysis, we then investigate the relative cost and effectiveness of the above-mentioned criteria by revisiting fundamental questions regarding the relationships between fault detection, test suite size, and control/data flow coverage. Although such questions have been partially investigated in previous studies, we can use a large number of mutants, which helps decrease the impact of random variation in our analysis and allows us to use a different analysis approach. Our results are then; compared with published studies, plausible reasons for the differences are provided, and the research leads us to suggest a way to tune the mutation analysis process to possible differences in fault detection probabilities in a specific environment

Adaptive Fault-Tolerant Tracking Control Against Actuator Faults With Application to Flight Control

Abstract: This paper deals with the problem of flight tracking control against actuator faults using the linear matrix inequality (LMI) method and adaptive method. An adaptive fault-tolerant flight controller design method is developed based on the online estimation of an eventual fault and the addition of a new control law to the normal control law in order to reduce the fault effect on the system without the need for a fault detection and isolation (FDI) mechanism. In the framework of LMI approach, the normal tracking performance of the resultant closed-loop system is optimized without any conservativeness and the states of fault modes asymptotically track those of the normal mode. A numerical example of an F-16 aircraft model and its simulation results are given

Distributed fault detection of wireless sensor networks

Abstract: Wireless Sensor Networks (WSNs) have become a new information collection and monitoring solution for a variety of applications. Faults occurring to sensor nodes are common due to the sensor device itself and the harsh environment where the sensor nodes are deployed. In order to ensure the network quality of service it is necessary for the WSN to be able to detect the faults and take actions to avoid further degradation of the service. The goal of this paper is to locate the faulty sensors in the wireless sensor networks. We propose and evaluate a localized fault detection algorithm to identify the faulty sensors. The implementation complexity of the algorithm is low and the probability of correct diagnosis is very high even in the existence of large fault sets. Simulation results show the algorithm can clearly identify the faulty sensors with high accuracy.

Fault detection and diagnosis based on modified independent component analysis

Abstract: A novel multivariate statistical process monitoring (MSPM) method based on modified independent component analysis (ICA) is proposed. ICA is a multivariate statistical tool to extract statistically independent components from observed data, which has drawn considerable attention in research fields such as neural networks, signal processing, and blind source separation. In this article, some drawbacks of the original ICA algorithm are analyzed and a modified ICA algorithm is developed for the purpose of MSPM. The basic idea of the approach is to use the modified ICA to extract some dominant independent components from normal operating process data and to combine them with statistical process monitoring techniques. Variable contribution plots to the monitoring statistics (T2 and SPE) are also developed for fault diagnosis. The proposed monitoring method is applied to fault detection and diagnosis in a wastewater treatment process, the Tennessee Eastman process, and a semiconductor etch process and is compared with conventional PCA monitoring methods. The monitoring results clearly illustrate the superiority of the proposed method. © 2006 American Institute of Chemical Engineers AIChE J, 2006

Fault detection and classification in transmission lines based on wavelet transform and ANN

Abstract: This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results

On the Use of Mutation Faults in Empirical Assessments of Test Case Prioritization Techniques

Abstract: Regression testing is an important activity in the software life cycle, but it can also be very expensive. To reduce the cost of regression testing, software testers may prioritize their test cases so that those which are more important, by some measure, are run earlier in the regression testing process. One potential goal of test case prioritization techniques is to increase a test suite's rate of fault detection (how quickly, in a run of its test cases, that test suite can detect faults). Previous work has shown that prioritization can improve a test suite's rate of fault detection, but the assessment of prioritization techniques has been limited primarily to hand-seeded faults, largely due to the belief that such faults are more realistic than automatically generated (mutation) faults. A recent empirical study, however, suggests that mutation faults can be representative of real faults and that the use of hand-seeded faults can be problematic for the validity of empirical results focusing on fault detection. We have therefore designed and performed two controlled experiments assessing the ability of prioritization techniques to improve the rate of fault detection of test case prioritization techniques, measured relative to mutation faults. Our results show that prioritization can be effective relative to the faults considered, and they expose ways in which that effectiveness can vary with characteristics of faults and test suites. More importantly, a comparison of our results with those collected using hand-seeded faults reveals several implications for researchers performing empirical studies of test case prioritization techniques in particular and testing techniques in general

Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines

Abstract: In this paper, the authors give a new model of squirrel-cage induction motors under stator and rotor faults. First, they study an original model that takes into account the effects of interturn faults resulting in the shorting of one or more circuits of stator-phase winding. They introduce, thus, additional parameters to explain the fault in the three stator phases. Then, they propose a new faulty model dedicated to broken rotor bars detection. The corresponding diagnosis procedure based on parameter estimation of the stator and rotor faulty model is proposed. The estimation technique is performed by taking into account prior information available on the safe system operating in nominal conditions. A special three-phase induction machine has been designed and constructed in order to simulate true faulty experiments. Experimental test results show good agreement and demonstrate the possibility of detection and localization of previous failures.

On the value of static analysis for fault detection in software

Abstract: No single software fault-detection technique is capable of addressing all fault-detection concerns. Similarly to software reviews and testing, static analysis tools (or automated static analysis) can be used to remove defects prior to release of a software product. To determine to what extent automated static analysis can help in the economic production of a high-quality product, we have analyzed static analysis faults and test and customer-reported failures for three large-scale industrial software systems developed at Nortel Networks. The data indicate that automated static analysis is an affordable means of software fault detection. Using the orthogonal defect classification scheme, we found that automated static analysis is effective at identifying assignment and checking faults, allowing the later software production phases to focus on more complex, functional, and algorithmic faults. A majority of the defects found by automated static analysis appear to be produced by a few key types of programmer errors and some of these types have the potential to cause security vulnerabilities. Statistical analysis results indicate the number of automated static analysis faults can be effective for identifying problem modules. Our results indicate static analysis tools are complementary to other fault-detection techniques for the economic production of a high-quality software product.

Fault Detection in a Multistage Gearbox by Demodulation of Motor Current Waveform

Abstract: Demodulation of vibration signal to detect faults in machinery has been a prominent prevalent technique that is discussed by a number of authors. This paper deals with the demodulation of the current signal of an induction motor driving a multistage gearbox for its fault detection. This multistage gearbox has three gear ratios, and thus, three rotating shafts and their corresponding gear mesh frequencies (GMFs). The gearbox is loaded electrically by a generator feeding an electrical resistance bank. Amplitude demodulation and frequency demodulation are applied to the current drawn by the induction motor for detecting the rotating shaft frequencies and GMFs, respectively. Discrete wavelet transform is applied to the demodulated current signal for denoising and removing the intervening neighboring features. Spectrum of a particular level, which comprises the GMFs, is used for gear fault detection

Diagnosability Analysis Based on Component-Supported Analytical Redundancy Relations

Abstract: It is commonly accepted that the requirements for maintenance and diagnosis should be considered at the earliest stages of design. For this reason, methods for analyzing the diagnosability of a system and determining which sensors are needed to achieve the desired degree of diagnosability are highly valued. This paper clarifies the different diagnosability properties of a system and proposes a model-based method for: 1) assessing the level of discriminability of a system, i.e., given a set of sensors, the number of faults that can be discriminated, and its degree of diagnosability, i.e., the discriminability level related to the total number of anticipated faults; and 2) characterizing and determining the minimal additional sensors that guarantee a specified degree of diagnosability. The method takes advantage of the concept of component-supported analytical redundancy relation, which considers recent results crossing over the fault detection and isolation and diagnosis communities. It uses a model of the system to analyze in an exhaustive manner the analytical redundancies associated with the availability of sensors and performs from that a full diagnosability assessment. The method is applied to an industrial smart actuator that was used as a benchmark in the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems European project

CORRESPONDENCE - Takagi-Sugeno Fuzzy-Model-Based Fault Detection for Networked Control Systems with Markov Delays

Abstract: A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches

Takagi-sugeno fuzzy-model-based fault detection for networked control systems with Markov delays

Abstract: A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches

Mechanical Load Fault Detection in Induction Motors by Stator Current Time-Frequency Analysis

Abstract: This paper examines the detection of mechanical faults in induction motors by an original use of stator current time-frequency analysis. Mechanical faults lead generally to periodic load torque oscillations. The influence of the torque oscillations on the induction motor stator current is studied using an analytical approach. The mechanical fault results in a sinusoidal phase modulation of the stator current, which is equivalent to a time-varying frequency. Based on these assumptions, several signal processing methods suitable for stator current signature analysis are discussed: classical spectral analysis, instantaneous frequency estimation, and the Wigner distribution. Experimental and simulation results validate the theoretical approach in steady-state operating conditions

Indoor multi-vehicle flight test bed for fault detection, isolation, and recovery

Abstract: This paper presents flight tests of a unique indoor, multi-vehicle testbed that was developed to study long duration UAV missions in a controlled environment. This testbed uses real hardware to examine research questions related to single and multi-vehicle health management, such as vehicle failures, refueling, and maintenance. The primary goal of the project is to embed health management into the full UAV planning system, thereby leading to improved overall mission performance, even when using simple aircraft that are prone to failures. The testbed has both aerial and ground vehicles that operate autonomously in a large test region and can be used to execute many different mission scenarios. The success of this testbed is largely related to our choice of vehicles, sensors, and the system’s command and control architecture, which has resulted in a testbed that is very simple to operate. This paper discusses this testbed infrastructure and presents flight test results from some of our most recent singleand multi-vehicle experiments.

Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis

Abstract: The purposes of multivariate statistical process control (MSPC) are to improve process operations by quickly detecting when process abnormalities have occurred and diagnosing the sources of the process abnormalities. In the area of semiconductor manufacturing, increased yield and improved product quality result from reducing the amount of wafers produced under suboptimal operating conditions. This paper presents a complete MSPC application method that combines recent contributions to the field, including multiway principal component analysis (PCA), recursive PCA, fault detection using a combined index, and fault contributions from Hotelling's T/sup 2/ statistic. In addition, a method for determining multiblock fault contributions to the combined index is introduced. The effectiveness of the system is demonstrated using postlithography metrology data and plasma stripper processing tool data.

Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors

Abstract: Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor-current spectrum. It has been shown that these broken-rotor-bar specific frequencies are located around the fundamental stator current frequency and are termed lower and upper sideband components. Broken-rotor-bar fault-detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple discriminant analysis (MDA) and artificial neural networks (ANNs) provide appropriate environments to develop such fault-detection schemes because of their multiinput-processing capabilities. This paper describes two fault-detection schemes for a broken-rotor-bar fault detection with a multiple signature processing and demonstrates that the multiple signature processing is more efficient than a single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA or ANN unit representing the complete operating load-torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA or ANN units, each unit representing a particular load-torque operating region. Fault-detection performance comparison between the MDA and the ANN with respect to the two schemes is investigated using the experimental data collected for a healthy and a broken-rotor-bar case. Partition scheme distributes the computational load and complexity of the large-scale single units in a monolith scheme to many smaller units, which results in the increase of the broken-rotor-bar fault-detection performance, as is confirmed with the experimental results

Automatic Instruction-Level Software-Only Recovery

Abstract: As chip densities and clock rates increase, processors are becoming more susceptible to transient faults that can affect program correctness. Computer architects have typically addressed reliability issues by adding redundant hardware, but these techniques are often too expensive to be used widely. Software-only reliability techniques have shown promise in their ability to protect against soft-errors without any hardware overhead. However, existing low-level software-only fault tolerance techniques have only addressed the problem of detecting faults, leaving recovery largely unaddressed. In this paper, we present the concept, implementation, and evaluation of automatic, instruction-level, software-only recovery techniques, as well as various specific techniques representing different trade-offs between reliability and performance. Our evaluation shows that these techniques fulfill the promises of instruction-level, software-only fault tolerance by offering a wide range of flexible recovery options.

A voltage based protection for micro-grids containing power electronic converters

Abstract: Protecting micro-grids containing micro-sources equipped with power electronics interfaces is a major challenge for engineers developing techniques to exploit renewable energy sources for electricity generation. Conventional techniques based on overcurrent protection have major limitations and new techniques have to be explored. The method described in this paper provides reliable and fast detection for different types of faults within the micro-grid. The micro-source output voltages are monitored and then transformed into dc quantities using the d-q reference frame. Any disturbance at the micro-source output due to a fault on the network will be reflected as disturbances in the d-q values. This disturbance is used to detect the fault and initiate the isolation the faulted section. Analysis and simulation results are presented for different types of faults within the microgrid.

High-impedance fault detection in distribution networks with use of wavelet-based algorithm

Abstract: A new simple and effective algorithm of arcing fault detection in distribution networks with the application of a wavelet transform technique is presented in this paper. The protection algorithm developed observes the phase displacement between wavelet coefficients calculated for zero-sequence voltage and current signals at a chosen high-level frequency. The final decision in regards to feeder switching off (or alarm issuing) is met either with a deterministic logic scheme or with the use of a neural net trained especially for that purpose. The developed wavelet-based high-impedance fault (HIF) detector has been tested with Electromagnetic Transients Program-Alternative Transients Program (ATP)-generated signals, exhibiting better performance than traditionally used algorithms and methods. The protection method proposed may be used for HIF detection independent of the network neutral-point grounding mode. The scheme proved to be robust against transients generated during normal events such as feeder energizing and de-energizing as well as capacitor bank switching

Method and system of detecting faults in a breathing assistance device

Abstract: A breathing assistance system (10) with functionality for detecting the existence of a fault condition may include a pressure detector (42), a flow detector (40) and a fault detection system (46). The pressure detector (42) may take pressure measurements, each measurement comprising a measurement of a gas flow rate in the breathing assistance system (10). The flow detector (40) may take flow rate measurements, each flow rate measurement comprising a measurement of has flow rate in the breathing assistance system (10). The fault detection system (46) may process the pressure measurements and/or flow rate measurements to determine the existence of a fault condition associated with the breathing assistance system (10).

Process plant monitoring based on multivariate statistical analysis and on-line process simulation

Abstract: Disclosed are systems and methods for on-line monitoring of operation of a process in connection with process measurements indicative of the operation of the process. In some cases, the operation of the process is simulated to generate model data indicative of a simulated representation of the operation of the process and based on the process measurements. A multivariate statistical analysis of the operation of the process is implemented based on the model data and the process measurements. The output data from the multivariate statistical analysis may then be evaluated during the operation of the process to enable the on-line monitoring of the process involving, for instance, fault detection via classification analysis of the output data.