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Fault Isolation (fault + isolation)

Distribution by Scientific Domains
Chemistry66%

Selected Abstracts

Nonstationary fault detection and diagnosis for multimode processes

AICHE JOURNAL, Issue 1 2010
Jialin Liu
Abstract Fault isolation based on data-driven approaches usually assume the abnormal event data will be formed into a new operating region, measuring the differences between normal and faulty states to identify the faulty variables. In practice, operators intervene in processes when they are aware of abnormalities occurring. The process behavior is nonstationary, whereas the operators are trying to bring it back to normal states. Therefore, the faulty variables have to be located in the first place when the process leaves its normal operating regions. For an industrial process, multiple normal operations are common. On the basis of the assumption that the operating data follow a Gaussian distribution within an operating region, the Gaussian mixture model is employed to extract a series of operating modes from the historical process data. The local statistic T2 and its normalized contribution chart have been derived for detecting abnormalities early and isolating faulty variables in this article. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source]

Fault isolation in nonlinear systems with structured partial principal component analysis and clustering analysis

THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 3 2000
Yunbing Huang
Abstract Partial principal component analysis (PCA) and parity relations are proven to be useful methods in fault isolation. To overcome the limitation of applying partial PCA to nonlinear problems, a new approach utilizing clustering analysis is proposed. By dividing a partial data set into smaller subsets, one can build more accurate PCA models with fewer principal components, and isolate faults with higher precision. Simulations on a 2 × 2 nonlinear system and the Tennessee Eastman (TE) process show the advantages of using the clustered partial PCA method over other nonlinear approaches. L'analyse des principaux constituants partielle et les relations de parité s'avèrent des méthodes utiles pour isoler les défaillances. Mais étant donné les limites d'application de l'analyse partielle des principaux constituants, on propose une nouvelle méthode reposant sur l'analyse de la formation des grappes. En divisant un jeu de données partielles en plusieurs sous-groupes plus petits, on peut créer des modèles d'analyse des principaux constituants plus précis avec un nombre de constituants moins importants et isoler les défaillances avec une meilleure précision. Les simulations sur un système non linéaire 2 × 2 et le procédé Tennessee Eastman (TE) montrent les avantages de la méthode d'analyse des principaux constituants partielle par grappes par rapport aux autres methodes non linéaires. [source]

On fault isolation by neural-networks-based parameter estimation techniques

EXPERT SYSTEMS, Issue 1 2007
Ramon Ferreiro Garcia
Abstract: The aim of the work is to exploit some aspects of functional approximation techniques in parameter estimation procedures applied on fault detection and isolation tasks using backpropagation neural networks as functional approximation devices. The major focus of this paper deals with the strategy used in the data selection task as applied to the determination of non-conventional process parameters, such as performance or process-efficiency indexes, which are difficult to acquire by direct measurement. The implementation and validation procedure on a real case study is carried out with the aid of the facilities supplied by commercial neural networks toolboxes, which manage databases, neural network structures and highly efficient training algorithms. [source]

Improved process monitoring using nonlinear principal component models,

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2008
David Antory
This paper presents two new approaches for use in complete process monitoring. The first concerns the identification of nonlinear principal component models. This involves the application of linear principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented. © 2008 Wiley Periodicals, Inc. [source]

Fault isolation in nonlinear systems with structured partial principal component analysis and clustering analysis

THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 3 2000
Yunbing Huang
Abstract Partial principal component analysis (PCA) and parity relations are proven to be useful methods in fault isolation. To overcome the limitation of applying partial PCA to nonlinear problems, a new approach utilizing clustering analysis is proposed. By dividing a partial data set into smaller subsets, one can build more accurate PCA models with fewer principal components, and isolate faults with higher precision. Simulations on a 2 × 2 nonlinear system and the Tennessee Eastman (TE) process show the advantages of using the clustered partial PCA method over other nonlinear approaches. L'analyse des principaux constituants partielle et les relations de parité s'avèrent des méthodes utiles pour isoler les défaillances. Mais étant donné les limites d'application de l'analyse partielle des principaux constituants, on propose une nouvelle méthode reposant sur l'analyse de la formation des grappes. En divisant un jeu de données partielles en plusieurs sous-groupes plus petits, on peut créer des modèles d'analyse des principaux constituants plus précis avec un nombre de constituants moins importants et isoler les défaillances avec une meilleure précision. Les simulations sur un système non linéaire 2 × 2 et le procédé Tennessee Eastman (TE) montrent les avantages de la méthode d'analyse des principaux constituants partielle par grappes par rapport aux autres methodes non linéaires. [source]

Fault detection and isolation for dynamic processes using recursive principal component analysis (PCA) based on filtering of signals

ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 6 2007
Jyh-Cheng Jeng
Abstract A systematic procedure for the fault detection and isolation of dynamic systems is presented. The inputs of the process first pass through the dynamic filters which represent the process dynamics. Then, principal component analysis (PCA) is applied to the data matrix consisting of these filtered signals and the process outputs for fault detection. In case of a fault being detected, owing to an artificial linear relationship existing in the data matrix, the last principal component (LPC) is adopted for fault isolation. A recursive algorithm for PCA based on rank-one matrix update of the covariance is derived to compute the LPC on line. Patterns of the LPC are devised to isolate these faults, which include constant-bias and high-frequency noises originating from sensor measurement, errors resulting from input disturbance and change in the process gain. Furthermore, the magnitude of the fault can also be identified from the computed LPC. An illustrative example is used to verify the effectiveness of the proposed method. Copyright © 2007 Curtin University of Technology and John Wiley & Sons, Ltd. [source]