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    PowerGen Gas Turbine Losses and Condition Monitoring: A Loss Data Based Study

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 002::page 21007
    Author:
    Zhou, Bin
    DOI: 10.1115/1.4031915
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In situ condition monitoring (CM) is a crucial element in protection and predictive maintenance of large rotating PowerGen equipment, such as gas turbines or steam turbines. In this work, selected gas turbine loss events occurring during a recent 10year period at our clients’ power generation plants were evaluated. For each loss event, a loss scenario or a chain of failures was outlined after investigating the available loss record. These loss events were then categorized based on the nature of the associated loss scenario. The study subsequently focused on the variables that could be monitored in realtime to detect the abnormal turbine operating conditions, such as vibration characteristics, temperature, pressure, quality of working fluids, and material degradations. These groups of CM variables were then matched with detectable failures in each loss event and prioritized based on their effectiveness for failure detection and prevention. The detectable loss events and the associated loss values were used in this evaluation process. The study finally concluded with a summary of findings and pathforward actions.
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      PowerGen Gas Turbine Losses and Condition Monitoring: A Loss Data Based Study

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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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    contributor authorZhou, Bin
    date accessioned2017-05-09T01:25:28Z
    date available2017-05-09T01:25:28Z
    date issued2016
    identifier issn2332-9017
    identifier otherRISK_2_2_021007.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/160173
    description abstractIn situ condition monitoring (CM) is a crucial element in protection and predictive maintenance of large rotating PowerGen equipment, such as gas turbines or steam turbines. In this work, selected gas turbine loss events occurring during a recent 10year period at our clients’ power generation plants were evaluated. For each loss event, a loss scenario or a chain of failures was outlined after investigating the available loss record. These loss events were then categorized based on the nature of the associated loss scenario. The study subsequently focused on the variables that could be monitored in realtime to detect the abnormal turbine operating conditions, such as vibration characteristics, temperature, pressure, quality of working fluids, and material degradations. These groups of CM variables were then matched with detectable failures in each loss event and prioritized based on their effectiveness for failure detection and prevention. The detectable loss events and the associated loss values were used in this evaluation process. The study finally concluded with a summary of findings and pathforward actions.
    publisherThe American Society of Mechanical Engineers (ASME)
    titlePowerGen Gas Turbine Losses and Condition Monitoring: A Loss Data Based Study
    typeJournal Paper
    journal volume2
    journal issue2
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
    identifier doi10.1115/1.4031915
    journal fristpage21007
    journal lastpage1
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 002
    contenttypeFulltext
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