Giovanni Bechini

Gas Turbine Diagnostics by Means of Convolutional Neural Networks Fed With Time Series Data Encoded as Images

Gas Turbine Diagnostics by Means of Convolutional Neural Networks Fed With Time Series Data Encoded As Images

Encoding Multivariate Time Series of Gas Turbine Data as Images to Improve Fault Detection Reliability

Machine Learning Approaches for the Prediction of Gas Turbine Transients

Unsupervised Methodology for the Prognostics of Gas Turbine Abrupt Faults

Methodology to Monitor Early Warnings Before Gas Turbine Trip

Methodology to Monitor Early Warnings Before Gas Turbine Trip

Application of Transfer Learning for the Prediction of Gas Turbine Trip

Prediction of Gas Turbine Trip by Combining Gas Path Measurements and Vibration Signals

Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Optimal Classifier to Detect Unit of Measure Inconsistency in Gas Turbine Sensors

Statistical Rule Extraction for Gas Turbine Trip Prediction

Influence of the trigger time window on the detection of gas turbine trip

Statistical Rule Extraction for Gas Turbine Trip Prediction

Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models

Detection of Unit of Measure Inconsistency in gas turbine sensors by means of Support Vector Machine classifier

Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip

Structured Methodology for Clustering Gas Turbine Transients by Means of Multivariate Time Series

Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models

Structured Methodology for Clustering Gas Turbine Transients by Means of Multi-Variate Time Series

Detection of Unit of Measure Inconsistency by Means of a Machine Learning Model

Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models

Development and Validation of a General and Robust Methodology for the Detection and Classification of Gas Turbine Sensor Faults

Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models

A General Diagnostic Methodology for Sensor Fault Detection, Classification and Overall Health State Assessment

Validation of an Advanced Diagnostic Methodology for the Identification and Classification of Gas Turbine Sensor Faults by Means of Field Data

Capability of the Bayesian Forecasting Method to Predict Field Time Series