June 9th, 2021, 9:45 AM EDT - 11:15 AM EDT
Controls, Diagnostics & Instrumentation Track
Deep Dive:
9:45 - 10:15 AM EDT, Paper No. GT2021-60116, “Anomaly Detection for Fleets of Industrial Equipment Utilizing Machine Learning With Applications to Power Plant Monitoring”
Deep Dive:
10:15 - 10:45 AM EDT, Paper No. GT2021-58578, “Considerations for the Extension of Gas Path Health Management Techniques to Electrified Aircraft Propulsion Systems”
Rapid Talk:
10:45 - 10:55 AM EDT, Paper No. GT2021-60020, “Adjustment and Validation of Monitoring System Algorithms on the Simulated Historical Data of an Aircraft Engine Fleet”
Rapid Talk:
10:55 - 11:05 AM EDT, Paper No. GT2021-59249, “A Lesson on Operationalizing Machine Learning for Predictive Maintenance of Gas Turbines”
Rapid Talk:
11:05 - 11:15 AM EDT, Paper No. GT2021-59289, “A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With Lime Method”
Presentations
| Participant Role | Details | Action |
|---|---|---|
| Submission | Anomaly Detection for Fleets of Industrial Equipment Utilizing Machine Learning With Applications to Power Plant Monitoring | View |
| Submission | Considerations for the Extension of Gas Path Health Management Techniques to Electrified Aircraft Propulsion Systems | View |
| Submission | Adjustment and Validation of Monitoring System Algorithms on the Simulated Historical Data of an Aircraft Engine Fleet | View |
| Submission | A Lesson on Operationalizing Machine Learning for Predictive Maintenance of Gas Turbines | View |
| Submission | A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With Lime Method | View |