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    Wyświetlanie 1-3 z 3
    Tytuł:
    Recognition of rotor damages in a DC motor using acoustic signals
    Autorzy:
    Głowacz, A.
    Głowacz, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/200081.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    acoustic signal
    motor
    machine
    fault diagnosis
    recognition
    sygnał akustyczny
    silnik
    maszyna
    diagnostyka błędu
    rozpoznanie
    Opis:
    Diagnosis of electrical direct current motors is essential for industrial plants. The emphasis is put on the development of diagnostic methods of solutions for capturing, processing and recognition of diagnostic signals. This paper presents a technique of early fault diagnosis of a DC motor. The proposed approach is based on acoustic signals. A real-world data of the DC motor were used in the analysis. The work provides an original feature extraction method called the shortened method of frequencies selection (SMoFS-15). The obtained results of the presented analysis show that the early fault diagnostic method can be used for monitoring electrical DC motors. The proposed method can also support other fault diagnosis methods based on thermal, current, and vibration signals.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2017, 65, 2; 187-194
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems
    Autorzy:
    Patton, R. J.
    Lopez-Toribio, C. J.
    Uppal, F. J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908290.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    metoda sztucznej inteligencji
    rozpoznanie błędu
    modelowanie rozmyte
    system rozmyty
    artificial intelligence methods
    fault diagnosis
    residual generation
    fuzzy modelling
    neuro-fuzzy systems
    Opis:
    Recent approaches to fault detection and isolation (FDI) for dynamic systems using methods of integrating quantitative and qualitative model information, based upon artificial intelligence (AI) techniques are surveyed. In this study, the use of AI methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained artificial neural network (ANN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. For example, fuzzy logic can be used together with state-space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 471-518
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Expert System Coupled With a Hierarchical Structure of Fuzzy Neural Networks for Fault Diagnosis
    Autorzy:
    Calado, J. M. F.
    Costa, I. S.
    Powiązania:
    https://bibliotekanauki.pl/articles/908283.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rozpoznanie błędu
    wykrywanie błędu
    system ekspertowy
    sieć neuronowa rozmyta
    fault diagnosis
    fault detection
    fault isolation
    shallow knowledge
    deep knowledge
    expert system
    fuzzy neural network
    abrupt faults
    incipient faults
    Opis:
    An on-line fault diagnosis system, designed to be robust to the normal transient behaviour of the process, is described. The overall system consists of an expert system cascade with a hierarchical structure of fuzzy neural networks, corresponding to a multi-stage fault detection and isolation system. The fault detection is performed through the expert system by means of fault detection heuristic rules, generated from deep and shallow knowledge of the process under consideration. If a fault is detected, the hierarchical structure of fuzzy neural networks starts and it performs the fault isolation task. The structure of this diagnosis system was designed to allow for the diagnosis of single and multiple simultaneous abrupt and incipient faults from only single abrupt fault symptoms. Also, it combines the advantages of both fuzzy reasoning and neural networks learning capacity. A continuous binary distillation column has been used as a test bed of the current approach. Single, double and triple simultaneous abrupt faults, as well as incipient faults, have been considered. The preliminary results obtained show a good accuracy, even in the case of multiple faults.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 667-687
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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