PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Hardware in the Loop Simulation of Gas Turbines: How Real Time Control System Design Tools Can Be Exploited Also to Generate Fault Cases to Train and Tune Data Based Diagnostic Systems.
Version 1
: Received: 22 March 2024 / Approved: 25 March 2024 / Online: 25 March 2024 (08:30:18 CET)
How to cite:
Brighenti, A.; Brighenti, C. Hardware in the Loop Simulation of Gas Turbines: How Real Time Control System Design Tools Can Be Exploited Also to Generate Fault Cases to Train and Tune Data Based Diagnostic Systems.. Preprints2024, 2024031446. https://doi.org/10.20944/preprints202403.1446.v1
Brighenti, A.; Brighenti, C. Hardware in the Loop Simulation of Gas Turbines: How Real Time Control System Design Tools Can Be Exploited Also to Generate Fault Cases to Train and Tune Data Based Diagnostic Systems.. Preprints 2024, 2024031446. https://doi.org/10.20944/preprints202403.1446.v1
Brighenti, A.; Brighenti, C. Hardware in the Loop Simulation of Gas Turbines: How Real Time Control System Design Tools Can Be Exploited Also to Generate Fault Cases to Train and Tune Data Based Diagnostic Systems.. Preprints2024, 2024031446. https://doi.org/10.20944/preprints202403.1446.v1
APA Style
Brighenti, A., & Brighenti, C. (2024). Hardware in the Loop Simulation of Gas Turbines: How Real Time Control System Design Tools Can Be Exploited Also to Generate Fault Cases to Train and Tune Data Based Diagnostic Systems.. Preprints. https://doi.org/10.20944/preprints202403.1446.v1
Chicago/Turabian Style
Brighenti, A. and Chiara Brighenti. 2024 "Hardware in the Loop Simulation of Gas Turbines: How Real Time Control System Design Tools Can Be Exploited Also to Generate Fault Cases to Train and Tune Data Based Diagnostic Systems." Preprints. https://doi.org/10.20944/preprints202403.1446.v1
Abstract
Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.
Keywords
HIL model(s); Dynamic simulation(s); Data-based modelling; Predictive diagnostics; Fault detection and isolation (FDI)
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
