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    Wyświetlanie 1-5 z 5
    Tytuł:
    Random projections and Hotelling’s T2 statistics for change detection in high-dimensional data streams
    Autorzy:
    Skubalska-Rafajłowicz, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/331099.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    change detection
    multidimensional control charts
    dimensionality reduction
    random projections
    process monitoring
    wykrywanie zmian
    karta kontrolna
    redukcja wymiarowości
    monitorowanie procesu
    Opis:
    The method of change (or anomaly) detection in high-dimensional discrete-time processes using a multivariate Hotelling chart is presented. We use normal random projections as a method of dimensionality reduction. We indicate diagnostic properties of the Hotelling control chart applied to data projected onto a random subspace of Rn. We examine the random projection method using artificial noisy image sequences as examples.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 2; 447-461
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamic Neural Networks for Process Modelling in Fault Detection and Isolation Systems
    Autorzy:
    Korbicz, J.
    Patan, K.
    Obuchowicz, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/908291.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    wykrywanie błędu
    sieć neuronowa dynamiczna
    modelowanie nieliniowe
    algorytm inteligentny
    fault detection
    dynamic neural networks
    non-linear modelling
    learning algorithms
    FL-classifier
    two-tank system
    Opis:
    A fault diagnosis scheme for unknown nonlinear dynamic systems with modules of residual generation and residual evaluation is considered. Main emphasis is placed upon designing a bank of neural networks with dynamic neurons that model a system diagnosed at normal and faulty operating points.To improve the quality of neural modelling, two optimization problems are included in the construction of such dynamic networks: searching for an optimal network architecture and the network training algorithm. To find a good solution, the effective well-known cascade-correlation algorithm is adapted here. The residuals generated by a bank of neural models are then evaluated by means of pattern classification. To illustrate the effectiveness of our approach, two applications are presented: a neural model of Narendra's system and a fault detection and identification system for the two-tank process.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 519-546
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A graph theory-based approach to the description of the process and the diagnostic system
    Autorzy:
    Kościelny, Jan Maciej
    Bartyś, Michał
    Syfert, Michał
    Sztyber, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/2124778.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    graph of the process
    graph of the diagnostic system
    fault detection
    fault isolation
    qualitative model
    limitations of diagnostic approaches
    wykres procesu
    system diagnostyczny
    wykrywanie uszkodzeń
    izolacja uszkodzeń
    model jakościowy
    Opis:
    The paper proposes an original, comprehensive, and methodically consistent graph theory-based approach to the description of the diagnosed process and the diagnosing system. The main baseline of the presented approach is in the dichotomous approach to diagnosing. It involves a separate description of both the process and the diagnostic system. This approach reflects the practice of designing implementable diagnostic systems. Thus, it can be seen as a proposal of a new, alternative, and, at the same time, flexible design procedure with great potential for applications. The primary motivation behind it was an attempt to circumvent the numerous limitations of well-known and well-established diagnosis approaches proposed by the communities working on fault detection and isolation (FDI) and artificial intelligence theories for diagnosis (DX). Accordingly, the paper identifies and provides an extensive discussion and a critical analysis of the existing limitations. Numerous examples and references to practical applications of the approach are indicated.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 2; 213--227
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonlinear model predictive control of a boiler unit: a fault tolerant control study
    Autorzy:
    Patan, K.
    Korbicz, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/331450.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rekurencyjna sieć neuronowa
    model procesu
    sterowanie predykcyjne
    detekcja uszkodzeń
    zbiornik przepływowy
    recurrent neural networks
    process model
    predictive control
    fault detection
    boiler unit
    Opis:
    This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 1; 225-237
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A data driven fault isolation method based on reference faulty situations with application to a nonlinear chemical process
    Autorzy:
    Ragot, José
    Mourot, Gilles
    Kallas, Maya
    Powiązania:
    https://bibliotekanauki.pl/articles/2172122.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    fault detection
    fault isolation
    nonlinear system
    data modelling
    kernel
    wykrywanie uszkodzeń
    izolacja uszkodzeń
    układ nieliniowy
    modelowanie danych
    Opis:
    The diagnosis of systems is one of the major steps in their control and its purpose is to determine the possible presence of dysfunctions, which affect the sensors and actuators associated with a system but also the internal components of the system itself. On the one hand, the diagnosis must therefore focus on the detection of a dysfunction and, on the other hand, on the physical localization of the dysfunction by specifying the component in a faulty situation, and then on its temporal localization. In this contribution, the emphasis is on the use of software redundancy applied to the detection of anomalies within the measurements collected in the system. The systems considered here are characterized by non-linear behaviours whose model is not known a priori. The proposed strategy therefore focuses on processing the data acquired on the system for which it is assumed that a healthy operating regime is known. Diagnostic procedures usually use this data corresponding to good operating regimes by comparing them with new situations that may contain faults. Our approach is fundamentally different in that the good functioning data allow us, by means of a non-linear prediction technique, to generate a lot of data that reflect all the faults under different excitation situations of the system. The database thus created characterizes the dysfunctions and then serves as a reference to be compared with real situations. This comparison, which then makes it possible to recognize the faulty situation, is based on a technique for evaluating the main angle between subspaces of system dysfunction situations. An important point of the discussion concerns the robustness and sensitivity of fault indicators. In particular, it is shown how, by non-linear combinations, it is possible to increase the size of these indicators in such a way as to facilitate the location of faults.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 635--655
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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