PowerGen Gas Turbine Losses and Condition Monitoring: A Loss Data Based StudySource: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 002::page 21007Author:Zhou, Bin
DOI: 10.1115/1.4031915Publisher: 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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contributor author | Zhou, Bin | |
date accessioned | 2017-05-09T01:25:28Z | |
date available | 2017-05-09T01:25:28Z | |
date issued | 2016 | |
identifier issn | 2332-9017 | |
identifier other | RISK_2_2_021007.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/160173 | |
description 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | PowerGen Gas Turbine Losses and Condition Monitoring: A Loss Data Based Study | |
type | Journal Paper | |
journal volume | 2 | |
journal issue | 2 | |
journal title | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering | |
identifier doi | 10.1115/1.4031915 | |
journal fristpage | 21007 | |
journal lastpage | 1 | |
tree | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 002 | |
contenttype | Fulltext |