1. Spacecraft Anomaly Analysis and Prediction System – SAAPS
Peter Wintoft1), Henrik Lundstedt1), Lars Eliasson2), Leif Kalla2), and Alain Hilgers3)
1)Swedish Institute of Space Physics – Lund 2)Swedish Institute of Space Physics – Kiruna 3)ESA/ESTEC

2. SAAPS Spacecraft Anomaly Analysis and Prediction System
ESA Contract 11974/96/NL/JG(SC):
Development of AI Methods in Spacecraft Anomaly Predictions
Extension of the SPEE study
Two year project (April June 2001)
Database and software

3. Purpose
Develop tools for the analysis and prediction of spacecraft anomalies.

4. Approach Statistical methods for the analysis.
Artificial intelligence (AI) based models, such as neural networks, for predictions.
Real time operation.
Database of space weather data and spacecraft anomalies.

5. The model

6. SAAPS Data Sources

7. The model

8. SAAM Spacecraft Anomaly Analysis Module
Plotting tools
Statistics
Superposed epoch analysis
Correlations (linear and entropy based)
Cluster analysis
Pattern definition and search
Guidelines
Estimate best prediction model

11. The model

12. SAPM Spacecraft Anomaly Prediction Module
Neural network based prediction models
Real time forecast
Connects to SAAM for analysis
Anomaly index (?) and/or
Spacecraft dependent anomaly predictions

13. SKp based predictions Satellite specific model (geostationary)
Kp(t-8*24h)
SKp(t-8d)
A(t+1d)
SKp(t)
Kp(t)
Satellite specific model (geostationary)
Fraction of correct classifications is 0.65 on balanced test set

14. Mutual information between average SKp and ESD anomaly data

15. Mutual information between SKp and ESD anomaly data

16. Anomaly index?

17. I(X;Y)/H(Y)=0.80
I(X;Y)/H(X)=0.55

18. Predicting MeV electron flux
Inputs:
Daily average solar wind velocity and density
Local time
Outputs:
Hourly average GOES-08 >2 MeV electron flux

19. Nowcast
1-day forecast

20. Daily
Hourly
Forecast
Observed NN

21. Summary Database with Analysis module to perform
solar wind data,
geosynchronous particle data,
geomagntic indices, and
anomaly data.
Analysis module to perform
event studies and
statistics.
Predictions module for
anomalies and
environment.

23. Prediction module