AEROSPACE ANALYTICS
An efficient approach to visualizing where and why structural anomalies occur across the flight envelope to reduce variation, instability, and risk.
LIVE WEBINAR
Session 2
Moving from Signal Monitoring to System Modeling
Date: Thursday, June 18
Time: 1:00 p.m. ET | 10:00 a.m. PT
Duration: 30 minutes
Registration: FREE
Level: No prior knowledge of advanced modeling required
Overview
Aerospace systems generate large volumes of telemetry and test data. While JMP can help with data access, the main challenge is extracting clear engineering insight to make confident decisions.
This session introduces a practical approach to move beyond signal monitoring and toward understanding how systems behave – including when that behavior changes – and what it means for performance and risk through data modeling.
Learn how to:
- Define expected system behavior from real data.
- Separate environmental effects from true system change.
- Detect efficiency loss and structural anomalies earlier.
- Identify changes in system relationships, not just signal shifts.
- Translate results into engineering insight for risk and decision making.
While most workflows focus on individual signals, thresholds, and static analysis, this session demonstrates how to understand system behavior and how to detect when it deviates from expected physics.
Using practical aerospace examples, discover how to model expected system behavior, isolate deviations using residuals, identify distinct operating regimes based on behavior, and connect analytical results directly to engineering interpretation. By using this approach, engineers can detect issues earlier, understand root causes more clearly, make more informed, risk-aware decisions.
Where it applies
- Propulsion and performance analysis.
- Structural and vibration behavior.
- Test and telemetry data interpretation.
- Anomaly detection and investigation.
- Reliability and risk workflows.
With JMP, you have an interactive environment for exploring multivariate relationships, building and comparing models, visualizing system behavior and deviations, and connecting analysis directly to engineering insight. No coding required.
Move from analyzing signals to understanding system behavior.
About the presenter
Kemal Oflus,
Principal Systems Engineer
Kemal Oflus is a Principal Systems Engineer for JMP Statistical Discovery, which creates interactive and highly visual statistical discovery software designed for scientists and engineers. He supports sales and customer development in the Southwest region of the U.S. as technical lead.
An applied physicist by training, Oflus previously worked as a rocket scientist using statistical methods to support risk assessment on the design and simulation of several projects for NASA and U.S. Air Force. He is a member of the American Society for Quality and the American Statistical Association. Oflus regularly delivers talks and keynotes on data mining and predictive modeling; he also teaches data science at the University of California Riverside as a part-time lecturer.