Data-Driven Model Selection Study for Long-Term Performance Deterioration of Gas Turbines
| Autor: | Avisekh Banerjee, Yuan Liu, Amar Kumar, Houman Hanachi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Model selection Multivariable calculus 05 social sciences ComputerApplications_COMPUTERSINOTHERSYSTEMS 01 natural sciences Reliability engineering Term (time) Data-driven 010309 optics Power rating Electricity generation 0502 economics and business 0103 physical sciences Fuel efficiency 050203 business & management Predictive modelling |
| Zdroj: | ICPHM |
| DOI: | 10.1109/icphm.2019.8819433 |
| Popis: | Performance of gas turbine engine (GTE) deteriorates with structural aging. The availability of operating data from GTE and capability to perform data analysis, provides an opportunity to identify long-term performance deterioration and relate to more difficult to detect structural degradation. In this work, performance analysis of a low power rating and partially loaded industrial GTE was carried out by using a model-free data analytic approach. A performance index (ratio of power generation to fuel consumption) is proposed as the metrics for monitoring the engine performance, and monitor the long-term degradation symptom. A comparative model selection study has been conducted among three multivariable models to select the best model describing long-term performance deterioration of the GTE. |
| Databáze: | OpenAIRE |
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