Recent developments in data mining techniques for anomaly detection make it possible to use the wealth of available archived spacecraft system data to produce advanced system health monitoring applications. These "data driven" applications are capable of characterizing and monitoring interactions between multiple parameters and can complement existing practice to provide valuable decision support for mission controllers. Data driven software tools, including Orca and the Inductive Monitoring System (IMS), have been successfully applied to mission operations for both the Space Shuttle and the International Space Station. Orca uses a nearest-neighbor approach to search for unusual data points in multivariate data sets by calculating the distance of each data point from neighboring points. The IMS tool uses a data mining technique called clustering to analyze archived spacecraft data and characterize nominal interactions between selected parameters. This characterization, or model, is compared with real time or archived system data to detect off nominal behavior. Augmenting traditional mission control software with advanced monitoring tools, such as Orca and IMS, can provide controllers with greater insight into the health and performance of the space systems under their watch. We will describe how such techniques have been applied to NASA mission control operations and discuss plans for future mission control system health monitoring software.
Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.
Many spacecraft provide an abundance of system status telemetry that is monitored in real time by ground personnel and archived to allow for further analysis. In the flight control room, controllers typically monitor these values using text or graphical displays that incorporate individual parameter limit checking or simple trend analysis. Recent developments in data mining techniques for anomaly detection make it possible to use the wealth of archived system data to produce more sophisticated system health monitoring applications. These "data driven" applications are capable of characterizing and monitoring interactions between multiple parameters and can complement existing practice to provide valuable decision support for mission controllers.Data driven software tools have been successfully applied to mission operations for both the Space Shuttle and the International Space Station. These tools have been applied to engineering analysis of spacecraft data to detect unusual events in the data, and to real-time system health monitoring in the flight control room. Augmenting traditional mission control software with advanced monitoring tools can provide controllers with greater insight into the health and performance of the space systems under their watch. Adding heuristic rule based methods that encode system knowledge obtained from seasoned mission controllers can also be helpful to less experienced personnel. We will describe how such techniques have been applied to NASA mission control operations and discuss plans for future mission control system health monitoring software systems.
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