Condition monitoring of gas turbine power plant using image processing (CMGTPPIP)

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

“…Determine operational change between steady-state and transients Manjit Verma and Amit Kumar [56] 2014 Qualitative and quantitative analysis for modelling and analysing machine behaviour Hamid Asgari et al [49] 2016 NARX Gas turbine's start-up dynamics' assessment Cristiano Hora Fontes and Otacílio Pereira [50] 2016 SPCA + Fuzzy C-means Operational pattern recognition Nallamilli P. G. Bhavani et al [53] 2016 ANN Intelligent sensor to monitor and control combustion quality Pogorelov G.I et al [43] 2017 RNN Dynamic performance assessment Abshukirov Zhandos and Jian Guo [47] 2017 Decision tree Behaviour analysis based on temperature evolution Josué Enríquez-Zárate et al [51] 2017 GSGP Model parameters' estimation Maria Grazia De Giorgi et al [44] 2018 ANN, SVM Model comparison for health status monitoring Jiao Liu et al [52] 2018 CNN Abnormal operation detection Farzan Majdani et al [57] 2018 ANN Inferential sensor for machine status assessment K. Sujatha, G. et al [54] 2019 ANN + PSO Exhaust gas estimation Hossein Shahabadi Farahani et al [45] 2020 TL Improvement of health monitoring system OCSVM, SVDD, Yanghui Tan et al [48] 2020 GKNN, LOF, IForest, Evaluation of operation conditions and ABOD Tryambak Gangopadhyay et al [55] 2021 3DCSAE Operational transition detection in a combustion system…”

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

“…It was verified that the effect of the EGT swirl was the key feature to differentiate normal and abnormal GT operations. An intelligent classifier was developed in [53] to monitor and control the combustion quality in power stations using an ANN method. Several image features such as the brightness of the flame, the area of high temperature, or the flame centroid were used to measure and monitor the temperature and the flue gas emission.…”

“…Yu Xhang et al [46] 2013 VBGMM Determine operational change between steadystate and transients Manjit Verma and Amit Kumar [56] 2014 PN Qualitative and quantitative analysis for modelling and analysing machine behaviour Hamid Asgari et al [49] 2016 NARX Gas turbine's start-up dynamics' assessment Cristiano Hora Fontes and Otacílio Pereira [50] 2016 SPCA + Fuzzy C-means Operational pattern recognition Nallamilli P. G. Bhavani et al [53] 2016 ANN Intelligent sensor to monitor and control combustion quality Pogorelov G.I et al [43] 2017 RNN Dynamic performance assessment Abshukirov Zhandos and Jian Guo [47] 2017 Decision tree Behaviour analysis based on temperature evolution Josué Enríquez-Zárate et al [51] 2017 GSGP Model parameters' estimation Maria Grazia De Giorgi et al [44] 2018 ANN, SVM Model comparison for health status monitoring Jiao Liu et al [52] 2018 CNN Abnormal operation detection Farzan Majdani et al [57] 2018 ANN Inferential sensor for machine status assessment K. Sujatha, G. et al [54] 2019 ANN + PSO Exhaust gas estimation Hossein Shahabadi Farahani et al [45] 2020 TL Improvement of health monitoring system Yanghui Tan et al [48] 2020 OCSVM, SVDD, GKNN, LOF, IForest, and ABOD…”