Application of Energy Processor Model for Diagnostic Symptom Limit Value Determination in Steam Turbines

Gałka, T.

doi:10.1006/mssp.1999.1226

Cited by 4 publications

(1 citation statement)

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“…For a given symptom operator, p(S) may be estimated from experimental data, providing that the available database is sufficiently large. In practice (Gałka, 1999) about 100 individual data points will allow for a reasonable estimation. Weibull and Fréchet operators usually yield S l values differing just by a few percent.…”

Section: Quantitative Diagnosismentioning

confidence: 99%

Vibration-Based Diagnostics of Steam Turbines

Gałka¹

2012

Mechanical Engineering

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Section: Quantitative Diagnosismentioning

confidence: 99%

Vibration-Based Diagnostics of Steam Turbines

Gałka¹

2012

Mechanical Engineering

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Large Rotating Machines

Gałka¹

2008

Encyclopedia of Structural Health Monitoring

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Large rotating machines are usually critical objects, so assessment of their condition is important for safety, economy, and reliability reasons. This is accomplished by use of both on‐line (permanently installed) and off‐line (periodical measurements with portable equipment) systems. Extensive on‐line monitoring systems that employ leading‐edge techniques (advanced data processing, artificial intelligence) are installed only on the largest and most important machines (e.g., base load steam turbines). Methods and procedures are aimed at both determining types and locations of failures (if any) and monitoring life consumption progress; the latter is particularly important with machines designed for long service life. Typical monitoring techniques involve various types of condition symptoms, but vibration‐based ones are most useful. Among them, absolute vibration spectra and relative vibration trajectories and vectors are most commonly employed. Due to complexity and many elementary vibration sources, a machine is typically represented by a set (or vector) of symptoms. Qualitative condition assessment is based on models developed for specific machine types. This involves finite element methods, numerical simulation of dynamic behavior, experimental modal analysis, and other state‐of‐the‐art techniques. Quantitative assessment can be accomplished by comparing actual symptom values with reference ones, which often have to be determined individually for each machine. Such determination can be based on the energy processor model and symptom reliability concept. Vibration‐based symptoms are usually augmented by process and condition parameters (temperature, pressure, relative displacement, etc.), especially during transients. Important information is also obtained from correlation analysis and long‐term symptom evolution assessment.

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Influence of Turbine Load on Vibration Pattern and Symptom Limit Value Determination Procedures

Gałka¹

2001

Condition Monitoring and Diagnostic Engineering Management

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Application of Energy Processor Model for Diagnostic Symptom Limit Value Determination in Steam Turbines

Cited by 4 publications

References 4 publications

Vibration-Based Diagnostics of Steam Turbines

Vibration-Based Diagnostics of Steam Turbines

Large Rotating Machines

Influence of Turbine Load on Vibration Pattern and Symptom Limit Value Determination Procedures

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