Use of probability indices for assessment of gas turbine power station reliability under commercial operation conditions

Mingazov, B. G.; Korobitsin, N. A.

doi:10.3103/s1068799810020170

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional analytical methods include, among others: Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Markov Analysis. An example of the Failure Mode and Effects Analysis (FMEA) supplemented by statistical failure rate model has been discussed by Mingazov and Korobitsin [29]. The results of such model application may allow to assess different gas turbine systems reliability.…”

Section: Introductionmentioning

confidence: 99%

Industrial gas turbine operating parameters monitoring and data-driven prediction

Pawełczyk¹,

Fulara²,

Sepe³

et al. 2020

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

The article reviews traditional and modern methods for prediction of gas turbine operating characteristics and its potential failures. Moreover, a comparison of Machine Learning based prediction models, including Artificial Neural Networks (ANN), is presented. The research focuses on High Pressure Compressor (HPC) recoup pressure level of 4th generation LM2500 gas generator (LM2500+G4) coupled with a 2-stage High Speed Power Turbine Module. The researched parameter is adjustable and may be used to balance net axial loads exerted on thrust bearing to ensure stable gas turbine operation, but its direct measurement is technically difficult implicating the need to indirect measurement via set of other gas turbine sensors. Input data for the research have been obtained from BHGE manufactured and monitored gas turbines and consists of real-time data extracted from industrial installations. Machine learning models trained using the data show less than 1% Mean Absolute Percentage Error (MAPE) as obtained with the use of Random Forest and Gradient Boosting Regression models. Multilayer Perceptron Artificial Neural Networks (MLP ANN) models are reviewed, and their performance checks inferior to Random Forest algorithm-based model. The importance of hyperparameter tuning and feature engineering is discussed.

show abstract

Section: Introductionmentioning

confidence: 99%