Data-Driven Fault Diagnosis of Once-through Benson Boilers

Fault diagnosis (FD) of once-through Benson boilers, as a crucial equipment of many thermal power plants, is of paramount importance to guarantee continuous performance. In this study, a new fault diagnosis methodology based on data-driven methods is presented to diagnose faults in once-through Bens...

Full description

Saved in:
Bibliographic Details
Published in2019 4th International Conference on System Reliability and Safety (ICSRS) pp. 345 - 354
Main Authors Azari, Mehdi Saman, Flammini, Francesco, Caporuscio, Mauro, Santini, Stefania
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
ANFIS
Boilers
Classification algorithms
data fusion
data-driven
Fault diagnosis
Inference algorithms
K-means
MST
once-through boiler
Robustness
Safety
Simulation
Stability analysis
Time measurement
Training
Online AccessGet full text
DOI10.1109/ICSRS48664.2019.8987699

Cover

More Information
Summary:Fault diagnosis (FD) of once-through Benson boilers, as a crucial equipment of many thermal power plants, is of paramount importance to guarantee continuous performance. In this study, a new fault diagnosis methodology based on data-driven methods is presented to diagnose faults in once-through Benson boilers. The present study tackles this issue by adopting a combination of data-driven methods to improve the robustness of FD blocks. For this purpose, one-class versions of minimum spanning tree and K-means algorithms are employed to handle the strong interaction between measurements and part load operation and also to reduce computation time and system training error. Furthermore, an adaptive neuro-fuzzy inference system algorithm is adopted to improve accuracy and robustness of the proposed fault diagnosing system by fusion of the output of minimum spanning tree (MST) and K-means algorithms. Performance of the presented scheme against six major faults is then assessed by analyzing several test scenario.
DOI:10.1109/ICSRS48664.2019.8987699