1. 1 Creating Situational Awareness with Data Trending and Monitoring Zhenping Li, J.P. Douglas, and Ken. Mitchell Arctic Slope Technical Services

2. 2 Challenges to Spacecraft Operations in New Missions Spacecraft Operations Maintaining spacecraft health and safety. Maintaining the instrument performance and accuracy Data Trending, Monitoring, and Engineering Analysis Data Trending: Determine data’s systematic behavior as distinguished from its random behavior The data trending is mostly done with statistical approach. Data Monitoring Static limits used to determine the status of a data point Engineering Analysis Involves the targeted review of spacecraft telemetry to Identify, Characterize, comprehend and Workaround an anomaly or failure. Mostly a manual process performed by satellite engineers. Challenges for new missions: Larger data volumes and limited resources. Spacecraft Maintenance OPS Con for current mission is no-longer working for the new mission.

3. 3 Machine Learning Approach Move beyond the current statistical trending: Performs the time dependent data trending using machine learning algorithm. Most of spacecraft data are time dependent. Move beyond the static limit in data monitoring: Defines dynamic limit based on the time dependent trend. Identify potential anomalies in real or near real time. Develop a trending and engineering analysis software tool with machine learning algorithm Deployable to different missions Benefits Provides an integrated approach to Data Trending and Engineering Analysis. Automate the trending and engineering analysis operations. Creates Situational Awareness: Does the engineering analysis in real or near real time.

4. 4 Situational Awareness and Machine Learning Situational Awareness Perception Monitor its environment Comprehension Making sense of data Projection Predict the future. Machine Learning Data Training for data pattern recognition Both data perception and comprehension Data prediction based on the established data patterns. Key: Detect the outlier/anomaly in real-time, and creating Actionable Data (or Intelligence), so that decision can be made either by human or by intelligent decision making tool. A necessary component for an autonomic ground system Enables self-configure, self-protection, self-healing, and self-optimizing. Machine Learning provides a systematic approach for situational awareness in ground system.

5. 5 Trend for time dependent datasets A time dependent dataset is characterized by its time dependent trend function standard deviation Data points within the following range are deemed to be following the trend: N is an user defined parameter based on the noise distributions and on the user's tolerance for isolating data outliers from the trend A data point outside the range is an outlier, which could indicate a potential anomaly Time dependent trending sets up the dynamic limits

6. 6 Machine Learning and Data Trending and Monitoring Machine Learning: Data Models or Algorithms that can learn from and make prediction on data. Two Stages of machine learning: Data training with a subset of data to establish a data model. Prediction: what to expect in the near future based on the established data patterns The Trending and Monitoring of time dependent datasets: Data Training→Data Trending to find a time dependent trend: –Function f ( t ) in a machine learning system is obtained by minimizing the error function Data Monitoring → Using the established data model to predict what to expect next. The dataset with an approximate periodical behavior: Weak dependence on n represents seasonal or long term trend.

7. 7 Data Training (Trending) Requirements: Systematic: datasets with arbitrary pattern and scale. Accurate: capture the data pattern complexities. Adaptive: capture long term or seasonal changes Efficient Data Training Approach: Initial Training: no previous knowledge of data patterns, when the system is deployed. –Done at the system deployment stage Retraining: The previous knowledge is known, minor adjustment to the data model. –Part of daily operations Training operations: performed daily with dataset of past two or three days

8. 8 Neural Network Algorithm Implementation Multilayer Forward-Feed and Back-propagation Network. A network with 2 hidden layer is implemented Good for any data pattern with good accuracy The output of a node at layer l with index j in a network is Data Scaling: convert a dataset into a scaled dataset with range {-1,1}. Using the statistical properties of a dataset as its scaling parameters. State Variables: A parameter set can be used to reconstruct the neural networks Network structure Weight parameters Scaling parameters

9. 9 Data Training with Neural Networks Initial Data Training: no prior knowledge of dataset Using random number as the initial values High accuracy requirements Using the no-linear least square fitting back-propagation algorithm Levenburg-Marquardt (LM) Back-propagation: Accurate, less efficient, not always converge, depending on the initial conditions. Use a modified gradient decent back-propagation to generate the initial parameters for LM algorithm. May need a few runs to get good data training results Data Retraining Captures the daily changes to the data patterns. Small, but significant sometimes. Previous states are used as the starting point. Requires the efficient algorithms for daily operations Using the modified gradient decent back-propagation Initial Training Blocks

10. 10 Data Trending Outputs Two data types with different data patterns have the same network structure GOES13 Imager Blackbody count GOES13 Sounder scan mirror temperature Red point: data Blue lines: function f (t) Orange Line: data bound

11. 11 Automated Anomaly Detection Each data point is evaluated with to determine its status (normal or outlier) The outlier example shown here can not be detected with the standard static limits Can be done during the data training, or the data monitoring Outlier example

12. 12 Software Implementation A common platform for trending algorithms that include the neural networks as well as other customized trending algorithms Component programming approach: implements trending engine as container and specific trending algorithms as component Well defined programming interface for data input No assumption made for data format. Retrieve the data sets as well as its meta data. Hierarchical database schema Database are setup for a specific project. Data trending for user defined variables Mathematical combination of input data types Satellite Data Trending and Monitoring Toolkit (SDTMT)

13. 13 Functionalities and Operation Concepts Three functions Perform daily trending operations Data monitoring Historical plots Two operation modes Batch mode for daily trending operations. Run trending engine through a script, and output to the trending archives Trending output is in netcdf format, including statistics, state variables Trending data plots, configured by users. Status reports, any outlier found during the trending process User interactive mode: Configuration management, database and trending archive viewer Perform manual trending for a specific data element Data monitoring, provide a dashboard to display of any variable defined in the database Historical plots.

14. 14 User Interactive Mode: Data Monitoring Color Scheme to represent the status of dataset

15. 15 Dashboard and Data Training Interactive Data Training: The random numbers input does not always converge, and may requires a few attempts to get good results Dashboard display plots for any data element defined in the database: the expected data value and data bounds vs. the actual data value.

16. 16 Summary and Applications Machine learning provides A systematic approach to create situational awareness. An automated and integrated approach to the data trending and engineering analysis A time dependent trend and dynamic limit A new area for R&D with potential applications Trending and monitoring the time dependent telemetry with diurnal behavior. Thermal and power properties Orbital profiles. Instrument data processing Both intermediate output and raw data input. Other potential areas of applications… To be deployed in GOES-R Ground System.