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Fault Diagnosis (fault + diagnosis)
Selected AbstractsFault Diagnosis Based on the Fuzzy-Recurrent Neural NetworkASIAN JOURNAL OF CONTROL, Issue 2 2001Zhao Xiang ABSTRACT A fuzzy-recurrent neural network (FRNN) has been constructed by adding some feedback connections to a feedforward fuzzy neural network (FNN). The FRNN expands the modeling ability of a FNN in order to deal with temporal problems. A basic concept of the FRNN is first to use process or expert knowledge, including appropriate fuzzy logic rules and membership functions, to construct an initial structure and to then use parameter-learning algorithms to fine-tune the membership functions and other parameters. Its recurrent property makes it suitable for dealing with temporal problems, such as on-line fault diagnosis. In addition, it also provides human-understandable meaning to the normal feedforward multilayer neural network, in which the internal units are always opaque to users. In a word, the trained FRNN has good interpreting ability and one-step-ahead predicting ability. To demonstrate the performance of the FRNN in diagnosis, a comparison is made with a conventional feedforward network. The efficiency of the FRNN is verified by the results. [source] A Model-Based Method for an Online Diagnostic Knowledge-Based SystemEXPERT SYSTEMS, Issue 3 2001Chrissanthi Angeli Fault diagnosis is very important for modern production technology and has received increasing theoretical and practical attention during the last few years. This paper presents a model-based diagnostic method for industrial systems. An online, real-time, deep knowledge based fault detection system has been developed by combining different development environments and tools. The system diagnoses, predicts and compensates faults by coupling symbolic and numerical data in a new environment suitable for the interaction of different sources of knowledge and has been successfully implemented and tested on a real hydraulic system. [source] Fault diagnosis of a simulated industrial gas turbine via identification approachINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2007S. Simani Abstract In this paper, a model-based procedure exploiting the analytical redundancy principle for the detection and isolation of faults on a simulated process is presented. The main point of the work consists of using an identification scheme in connection with dynamic observer and Kalman filter designs for diagnostic purpose. The errors-in-variables identification technique and output estimation approach for residual generation are in particular advantageous in terms of solution complexity and performance achievement. The proposed tools are analysed and tested on a single-shaft industrial gas turbine MATLAB/SIMULINK® simulator in the presence of disturbances, i.e. measurement errors and modelling mismatch. Selected performance criteria are used together with Monte-Carlo simulations for robustness and performance evaluation. The suggested technique can constitute the design methodology realising a reliable approach for real application of industrial process FDI. Copyright © 2006 John Wiley & Sons, Ltd. [source] Online expert systems for fault diagnosis in technical processesEXPERT SYSTEMS, Issue 2 2008Chrissanthi Angeli Abstract: It is generally accepted that there has been an increasing interest in online fault detection and diagnosis techniques for technical processes during the last few years. These techniques come from the artificial intelligence field or are classical numerical methods in combination with artificial intelligence methods. This paper presents a survey of recent research work in online expert systems for fault detection and diagnosis in technical processes. In addition, a short reference to other recent artificial intelligence methods for online fault detection is included and the main advantages and limitations of each method are illustrated. [source] An algorithm for multiple fault diagnosis in analogue circuitsINTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, Issue 6 2006M. Tadeusiewicz Abstract The paper deals with multiple fault diagnosis of analogue AC or DC circuits with limited accessible terminals for excitation and measurement and brings an algorithm for identificating faulty elements and evaluating their parameters. The main achievement is a method enabling us to efficiently identify faulty elements. For this purpose some testing equations are derived playing a key role in identification of possibly faulty elements which are next verified using a test of acceptance. The proposed approach is described in detail for double fault diagnosis. Also extension to triple fault diagnosis is given. Although the method pertains to linear circuits, some aspects of multiple fault diagnosis of non-linear circuits can be also performed using the small signal approach. Two numerical examples illustrate the proposed method and show its efficiency. Copyright © 2006 John Wiley & Sons, Ltd. [source] Frequency-domain grouping robust fault diagnosis for analog circuits with uncertaintiesINTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, Issue 1 2002Yuen-Haw Chang Abstract A new frequency-domain grouping robust fault diagnosis (GRFD) scheme, based on the design for both grouping-robust estimation/evaluation of component variation rate and Boolean-based decision process, is proposed for solving fault diagnosis of large-scale analog circuits with uncertainties, including both component tolerances and measurement errors. In this scheme, first, grouping-robust estimators for one partitioning of all component groups, designed by using grouping-full-column rank output measurement technique, are employed to estimate the component variation rate. Secondly, based on the faulty possibility judgment policy suggested in this research, grouping-robust evaluators are employed for the careful evaluation of component variation rate in order to reduce the effect of both component tolerances and measurement errors on the diagnostic test result for one partitioning. Next, both repeating the same one-partitioning GRFD for a variety of partitionings and combining Boolean-based decision process, an overall GRFD scheme is proposed for achieving analog fault diagnosis. Finally, a fault diagnosis example of a large-scale analog circuit is illustrated. The results indicate that without any a priori information about the type of component failure, a precise t-diagnosable case for a large-scale circuit with component tolerances and measurement errors is accomplished at the lowest measurement cost. Copyright © 2001 John Wiley & Sons, Ltd. [source] Plant diagnostics by transient classification: The ALADDIN approachINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2002Davide Roverso Any action taken on a plant, for example in response to an abnormal situation or in reaction to unsafe conditions, relies on the ability to identify the state and dynamics of operation of the plant. Although there might be hundreds or even thousands of measurements in a plant, there are generally few events occurring. The data from these measurements must be mapped into appropriate descriptions of the occurring event(s), which in most cases is a difficult task. The real-time history of scores of variables can be displayed and monitored in most computerized plant monitoring and control systems. However, whereas a simple visual inspection of displayed trends is generally sufficient to allow the operator to confirm the plant status during normal, steady-state operations, when the plant is subject to deviations due to anomalies or faults, the displayed trends of interacting variables can be very difficult to interpret, either because the changes are too subtle, or because the changes are too fast. In this article we describe the ALADDIN methodology for dynamic event recognition and fault diagnosis, which combines techniques such as recurrent neural network ensembles, wavelet on-line pre-processing (WOLP), and autonomous recursive task decomposition (ARTD), in an attempt to improve the practical applicability and scalability of this type of system to real processes and machinery. © 2002 Wiley Periodicals, Inc. [source] UniFAFF: a unified framework for implementing autonomic fault management and failure detection for self-managing networksINTERNATIONAL JOURNAL OF NETWORK MANAGEMENT, Issue 4 2009Ranganai Chaparadza Today's network management, as known within the Fault, Configuration, Accounting, Performance, Security (FCAPS) management framework, is moving towards the definition and implementation of ,self-managing' network functions, with the aim of eliminating or drastically reducing human intervention in some of the complex aspects or daunting tasks of network management. The fault management plane of the FCAPS framework deals with the following functions: fault detection, fault diagnosis, localization or isolation, and fault removal. Task automation is at the very heart of self-managing (autonomic) nodes and networks, meaning that all functions and processes related to fault management must be automated as much as possible within the functionalities of self-managing (autonomic) nodes and networks, in order for us to talk about autonomic fault management. At this point in time there are projects calling for implementing new network architectures that are flexible to support on-demand functional composition for context- or situation-aware networking. A number of such projects have started, under the umbrella of the so-called clean-slate network designs. Therefore, this calls for open frameworks for implementing self-managing (autonomic) functions across each of the traditional FCAPS management planes. This paper presents a unified framework for implementing autonomic fault management and failure detection for self-managing networks, a framework we are calling UniFAFF. Copyright © 2008 John Wiley & Sons, Ltd. [source] Using temporal correlation for fault localization in dynamically changing networksINTERNATIONAL JOURNAL OF NETWORK MANAGEMENT, Issue 4 2008Maitreya Natu A mobile ad hoc network creates a dynamic environment where node mobility can cause periodic changes in routes. Most existing fault localization algorithms assume availability of a complete and/or deterministic dependency model. Such assumptions cannot be made in the dynamically changing networks. This paper is aimed at developing a fault diagnosis architecture and algorithm to address the issue of dynamically changing dependencies in networks. We propose an architecture to capture the changes in dependencies and introduce a temporal correlation algorithm to perform fault diagnosis with the dynamically changing dependency information. We present an experimental evaluation of our work through simulation results using Qualnet. Copyright © 2007 John Wiley & Sons, Ltd. [source] Fault estimation,a standard problem approachINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2002J. Stoustrup This paper presents a range of optimization based approaches to fault diagnosis. A variety of fault diagnosis problems are reformulated in the so-called standard problem set-up introduced in the literature on robust control. Once the standard problem formulations are given, the fault diagnosis problems can be solved by standard optimization techniques. The proposed methods include (1) fault diagnosis (fault estimation, (FE)) for systems with model uncertainties; FE for systems with parametric faults, and FE for a class of nonlinear systems. Copyright © 2002 John Wiley & Sons, Ltd. [source] Fault detection in nonlinear continuous-time systems with uncertain parametersAICHE JOURNAL, Issue 9 2008Youdong Lin Abstract In model-based fault diagnosis for dynamic systems with uncertain parameters, an envelope of all fault-free behaviors can be determined from the model and used as a reference for detecting faults. We demonstrate here a method for generating an envelope that is rigorously guaranteed to be complete, but without significant overestimation. The method is based on an interval approach, but uses Taylor models to reduce the overestimation often associated with interval methods. To speed fault detection, a method that uses bounded-error measurement data and a constraint propagation procedure is proposed for shrinking the envelope. Several fault detection scenarios involving nonlinear, continuous-time systems are used to evaluate this approach. © 2008 American Institute of Chemical Engineers AIChE J, 2008 [source] Principal-component analysis of multiscale data for process monitoring and fault diagnosisAICHE JOURNAL, Issue 11 2004Seongkyu Yoon Abstract An approach is presented to multivariate statistical process control (MSPC) for process monitoring and fault diagnosis based on principal-component analysis (PCA) models of multiscale data. Process measurements, representing the cumulative effects of many underlying process phenomena, are decomposed by applying multiresolution analysis (MRA) by wavelet transformations. The decomposed process measurements are rearranged according to their scales, and PCA is applied to these multiscale data to capture process variable correlations occurring at different scales. Choosing an orthonormal mother wavelet allows each principal component to be a function of the process variables at only one scale level. The proposed method is discussed in the context of other multiscale approaches, and illustrated in detail using simulated data from a continuous stirred tank reactor (CSTR) system. A major contribution of the paper is to extend fault isolation methods based on contribution plots to multiscale approaches. In particular, once a fault is detected, the contributions of the variations at each scale to the fault are computed. These scale contributions can be very helpful in isolating faults that occur mainly at a single scale. For those scales having large contributions to the fault, one can further compute the variable contributions to those scales, thereby making fault diagnosis much easier. A comparison study is done through Monte Carlo simulation. The proposed method can enhance fault detection and isolation (FDI) performance when the frequency content of a fault effect is confined to a narrow-frequency band. However, when the fault frequency content is not localized, the multiscale approaches perform very comparably to the standard single-scale approaches, and offer no real advantage. © 2004 American Institute of Chemical Engineers AIChE J, 50: 2891,2903, 2004 [source] Direct near-field antenna testing and fault diagnosis by a silicon-probe-based optical sensing techniqueMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 2 2003R. Massa Abstract A non-perturbing, fast, low-cost probe for near-field measurements is presented in this paper. The new sensing technique's capabilities in antenna fault diagnosis and direct measurement of field intensity distribution at subwavelength distance from the sources are demonstrated by test measurement on different microwave antennas and applicators. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 38: 95,98, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10981 [source] Methodology for the optimal component selection of electronic devices under reliability and cost constraintsQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2007E. P. Zafiropoulos Abstract The objective of this paper is to present an efficient computational methodology for the reliability optimization of electronic devices under cost constraints. The system modeling for calculating the reliability indices of the electronic devices is based on Bayesian networks using the fault tree approach, in order to overcome the limitations of the series,parallel topology of the reliability block diagrams. Furthermore, the Bayesian network modeling for the reliability analysis provides greater flexibility for representing multiple failure modes and dependent failure events, and simplifies fault diagnosis and reliability allocation. The optimal selection of components is obtained using the simulated annealing algorithm, which has proved to be highly efficient in complex optimization problems where gradient-based methods can not be applied. The reliability modeling and optimization methodology was implemented into a computer program in Matlab using a Bayesian network toolbox. The methodology was applied for the optimal selection of components for an electrical switch of power installations under reliability and cost constraints. The full enumeration of the solution space was calculated in order to demonstrate the efficiency of the proposed optimization algorithm. The results obtained are excellent since a near optimum solution was found in a small fraction of the time needed for the complete enumeration (3%). All the optimum solutions found during consecutive runs of the optimization algorithm lay in the top 0.3% of the solutions that satisfy the reliability and cost constraints. Copyright © 2007 John Wiley & Sons, Ltd. [source] Fault Diagnosis Based on the Fuzzy-Recurrent Neural NetworkASIAN JOURNAL OF CONTROL, Issue 2 2001Zhao Xiang ABSTRACT A fuzzy-recurrent neural network (FRNN) has been constructed by adding some feedback connections to a feedforward fuzzy neural network (FNN). The FRNN expands the modeling ability of a FNN in order to deal with temporal problems. A basic concept of the FRNN is first to use process or expert knowledge, including appropriate fuzzy logic rules and membership functions, to construct an initial structure and to then use parameter-learning algorithms to fine-tune the membership functions and other parameters. Its recurrent property makes it suitable for dealing with temporal problems, such as on-line fault diagnosis. In addition, it also provides human-understandable meaning to the normal feedforward multilayer neural network, in which the internal units are always opaque to users. In a word, the trained FRNN has good interpreting ability and one-step-ahead predicting ability. To demonstrate the performance of the FRNN in diagnosis, a comparison is made with a conventional feedforward network. The efficiency of the FRNN is verified by the results. [source] Application of Multivariate Data Analysis for Identification and Successful Resolution of a Root Cause for a Bioprocessing ApplicationBIOTECHNOLOGY PROGRESS, Issue 3 2008Alime Ozlem Kirdar Multivariate Data Analysis (MVDA) can be used for supporting key activities required for successful bioprocessing. These activities include process characterization, process scale-up, process monitoring, fault diagnosis and root cause analysis. This paper examines an application of MVDA towards root cause analysis for identifying scale-up differences and parameter interactions that adversely impact cell culture process performance. Multivariate data analysis and modeling were performed using data from small-scale (2 L), pilot-scale (2,000 L) and commercial-scale (15,000 L) batches. The input parameters examined included bioreactor pCO2, glucose, lactate, ammonium, raw materials and seed inocula. The output parameters included product attributes, product titer, viable cell density, cell viability and osmolality. Time course performance variables (daily, initial, peak and end point) were also evaluated. Application of MVDA as a diagnostic tool was successful in identifying the root cause and designing experimental conditions to demonstrate and correct it. Process parameters and their interactions that adversely impact cell culture performance and product attributes were successfully identified. MVDA was successfully used as an effective tool for collating process knowledge and increasing process understanding. [source] | |||||||||||||