2012
DOI: 10.1109/tr.2012.2209257
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Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

Abstract: This version is available at https://strathprints.strath.ac.uk/41099/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any pro… Show more

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Cited by 9 publications
(5 citation statements)
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References 23 publications
(28 reference statements)
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“…Splitting up the time series data into various timesteps, or segments, based on the information provided by the expert, each data stream/channel timestep is assigned a symbol which is either rising, falling, fluctuating or stable. The assigning of the symbols is performed using a technique based on Signal to Symbol transformation [8] which has been successfully used for rotating plant in the nuclear industry previously [3]. For this application, the symbols are assessed by first calculating the average of the first and last 10% of the timestep, a comparison is then performed to determine which of the following four categories best describes the timestep.…”
Section: Methodsmentioning
confidence: 99%
“…Splitting up the time series data into various timesteps, or segments, based on the information provided by the expert, each data stream/channel timestep is assigned a symbol which is either rising, falling, fluctuating or stable. The assigning of the symbols is performed using a technique based on Signal to Symbol transformation [8] which has been successfully used for rotating plant in the nuclear industry previously [3]. For this application, the symbols are assessed by first calculating the average of the first and last 10% of the timestep, a comparison is then performed to determine which of the following four categories best describes the timestep.…”
Section: Methodsmentioning
confidence: 99%
“…2. The first step in the approach is to perform signal to symbol transformation (SST) [12] on the raw timeseries data for each datastream. This segments the data into discrete time intervals, each time interval is then assigned a symbol based on several calculations.…”
Section: Methodsmentioning
confidence: 99%
“…Due to the individual rules being based on trends in the data it is necessary to have a pre-processing stage to calculate these trends. The approach adopted for this application was to use a signal-to-symbol transformation approach [1]. Splitting up the trace into various timesteps based on the information provided by the expert, each timestep is assigned a symbol which is either rising, falling, fluctuating or stable.…”
Section: Methodsmentioning
confidence: 99%