**6. Conclusion**

This chapter shows an experimental implementation of telemetry data processing to obtain a hidden event using a data mining technique. As a concrete example, detection of signs of actual satellite attitude sensor hardware failure was considered.

As preparation of treatment of practical data analysis commonly performed in satellite operations, a basic concept and practice of quantitative telemetry data analysis was demonstrated using REIMEI satellite data. By this preparation, the reader understood what the FOG bias drift means and how it can be estimated from telemetry data of attitude motion. Then, an SVM was designed and tested to monitor the instability of the FOGs in the REIMEI satellite using 2 years of telemetry data. The result shows that the SVM can detect changes in bias with a simple linear kernel. Thus, the classification part of the SVM logic can be implemented on the flight software without difficulty. As a hint for actual implementation onboard software, the three-layer architecture for onboard software was explained.

The flight software version of the SVM using a script engine has not yet been tested in the REIMEI satellite, and the author is waiting for an opportunity to carry out an in-flight experiment. It appears that SVMs can be used as a standard autonomous software component not only for onboard software even in small satellite systems but also for monitoring telemetry data. Thus, the author conducted an experiment to export this concept of system monitoring onto an autonomous underwater vehicle as concept verification activities and obtain sound results as it was expected. However, the most fruitful result expected area seems to space systems and it is the final destination of this research.
