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Creating Situational Awareness with DataTrending and Monitoring
Zhenping Li, J.P. Douglas, and Ken. Mitchell
Arctic Slope Technical Services
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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 forthe new mission.
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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 machinelearning 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.
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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 patternrecognition
•Both data perception andcomprehension
•Data prediction based on theestablished 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 byintelligent 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 awarenessin ground system.
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Trend for time dependent datasets
•A time dependent dataset is characterized by its timedependent trend
•function
•standard deviation
•Data points within the following range are deemed to befollowing the trend:
•N is an user defined parameter based on the noise distributions andon the user's tolerance for isolating data outliers from the trend
•A data point outside the range is an outlier, which could indicate apotential anomaly
•Time dependent trending sets up the dynamic limits
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Machine Learning and Data Trending and Monitoring
•Machine Learning: Data Models or Algorithms that can learnfrom 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 establisheddata 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 theerror function
•Data Monitoring → Using the established data model to predict what toexpect next.
•The dataset with an approximate periodical behavior:
•Weak dependence on n represents seasonal or long term trend.
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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
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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 toreconstruct the neural networks
•Network structure
•Weight parameters
•Scaling parameters
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Data Training with Neural Networks
•Initial Data Training: no prior knowledge ofdataset
•Using random number as the initial values
•High accuracy requirements
•Using the no-linear least square fitting back-propagationalgorithm
•Levenburg-Marquardt (LM) Back-propagation:
•Accurate, less efficient, not always converge, depending onthe initial conditions.
•Use a modified gradient decent back-propagation togenerate 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
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Data Trending Outputs
Two data typeswith different datapatterns have thesame networkstructure
GOES13 Imager
Blackbody count
GOES13 Sounder scan
mirror temperature
Red point: data
Blue lines: function f(t)
Orange Line: data bound
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Automated Anomaly Detection
•Each data point is evaluated with todetermine its status (normal or outlier)
•The outlier example shown here can not be detected with the standardstatic limits
•Can be done during the data training, or the data monitoring
Outlierexample
Outlierexample
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Software Implementation
•A common platform for trendingalgorithms that include the neuralnetworks as well as other customizedtrending algorithms
•Component programming approach:implements trending engine as container andspecific trending algorithms as component
•Well defined programming interface fordata 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 MonitoringToolkit (SDTMT)
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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 ascript, 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 thedatabase
•Historical plots.
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User Interactive Mode: Data Monitoring
Color Scheme to represent the status of dataset
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Dashboard and Data Training
Interactive Data Training:
The random numbersinput does not alwaysconverge, and mayrequires a few attemptsto get good results
Dashboard display plotsfor any data elementdefined in the database:the expected data valueand data bounds vs. theactual data value.
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Summary and Applications
•Machine learning provides
•A systematic approach to create situational awareness.
•An automated and integrated approach to the data trending andengineering 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 diurnalbehavior.
•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.