Browsing by Author "Pozo Garcia, Octavio"

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  • An autonomous valve stiction detection system based on data characterization
    (2013) Zakharov, Alexey; Zattoni, Elena; Xie, Lei; Pozo Garcia, Octavio; Jämsä-Jounela, Sirkka-Liisa
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This paper proposes a valve stiction detection system which selects valve stiction detection algorithms based on characterizations of the data. For this purpose, novel data feature indexes are proposed, which quantify the presence of oscillations, meannonstationarity, noise and nonlinearities in a given data sequence. The selection is then performed according to the conditions on the index values in which each method can be applied successfully. Finally, the stiction detection decision is given by combining the detection decisions made by the selected methods. The paper ends demonstrating the effectiveness of the proposed valve stiction detection system with benchmark industrial data.
  • Classification and analysis of faults and their diagnosis methods in the process industry
    (2011) Pozo Garcia, Octavio
     School of Chemical Engineering | Master's thesis
  • Integrated FDD system for valve stiction in a paperboard machine
    (2013) Pozo Garcia, Octavio; Tikkala, Vesa-Matti; Zakharov, Alexey; Jämsä-Jounela, Sirkka-Liisa
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The performance of a modern industrial plant can be severely affected by the performance of its key devices, such as valves. In particular, valve stiction can cause poor performance in control loops and can consequently lower the efficiency of the plant and the quality of the product. This paper presents an integrated FDD system for valve stiction which employs various FDD methods in a parallel configuration. A reliability index was integrated into each method in order to estimate their degree of influence in the final diagnosis of the system. Each method and the integrated system were tested using industrial data.
  • Outline of a fault diagnosis system for a large-scale board machine
    (2012) Jämsä-Jounela, Sirkka-Liisa; Tikkala, Vesa-Matti; Zakharov, Alexey; Pozo Garcia, Octavio; Laavi, Helena; Myller, Tommi; Kulomaa, Tomi; Hämäläinen, Veikko
     School of Chemical Technology | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Global competition forces process industries to continuously optimize plant operation. One of the latest trends for efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition, and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed.
  • Outline of a fault diagnosis system for a large-scale board machine
    (2013) Jämsä-Jounela, Sirkka-Liisa; Tikkala, Vesa-Matti; Zakharov, Alexey; Pozo Garcia, Octavio; Laavi, Helena; Myller, Tommi; Kulomaa, Tomi; Hämäläinen, Veikko
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Global competition forces process industries to continuously optimize plant operation. One of the latest trends for efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition, and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed.