Research Papers

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

[+] Author and Article Information
Bin Zhou

Risk, Reliability and Failure Prevention Area, FM Global Research,
1151 Boston-Providence Turnpike, Norwood, MA 02062
e-mail: bin.zhou@fmglobal.com

Manuscript received February 23, 2015; final manuscript received October 29, 2015; published online January 4, 2016. Assoc. Editor: Chimba Mkandawire.

ASME J. Risk Uncertainty Part B 2(2), 021007 (Jan 04, 2016) (6 pages) Paper No: RISK-15-1024; doi: 10.1115/1.4031915 History: Received February 23, 2015; Accepted October 29, 2015

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 10-year 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 real-time 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 path-forward actions.

Copyright © 2016 by ASME
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Fig. 1

Percentages of total loss value and loss count by loss categories

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Fig. 2

Normalized average loss value by loss categories

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Fig. 3

Compressor versus turbine losses

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Fig. 4

Blade vibrations: cause and effect

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Fig. 5

Blade failure scenario and monitoring variables

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Fig. 6

Percentages of total loss value and loss counts detectable by groups of CM variables

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Fig. 7

Blade and rotor vibration monitoring




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