AIAA Infotech@Aerospace Conference 2009
DOI: 10.2514/6.2009-1909
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General Purpose Data-Driven System Monitoring for Space Operations

Abstract: 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 s… Show more

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Cited by 44 publications
(32 citation statements)
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“…Some details on SequenceMiner will be provided in Section 3.3. Other anomaly detection methods, such as Orca [3] and the Inductive Monitoring System (IMS) [12], find anomalies in multivariate continuous data. Both Orca and IMS are distance-based anomaly detection methods, in that they use a metric related to distance, such as the average Euclidean distance to its k-nearest neighbors, to assess the anomalousness of a point.…”
Section: Current State-of-the-art Algorithms On Anomaly Detectionmentioning
confidence: 99%
“…Some details on SequenceMiner will be provided in Section 3.3. Other anomaly detection methods, such as Orca [3] and the Inductive Monitoring System (IMS) [12], find anomalies in multivariate continuous data. Both Orca and IMS are distance-based anomaly detection methods, in that they use a metric related to distance, such as the average Euclidean distance to its k-nearest neighbors, to assess the anomalousness of a point.…”
Section: Current State-of-the-art Algorithms On Anomaly Detectionmentioning
confidence: 99%
“…The IMS [19,20] is similar to Orca in that it is distance-based. Like Orca, it uses Euclidean distance as its distance metric.…”
Section: Inductive Monitoring Systemmentioning
confidence: 99%
“…We therefore decided to use unsupervised anomaly detection algorithms, since they do not require labeled examples of anomalies. This article presents nine anomalies detected by four unsupervised anomaly detection algorithms: Orca [17], GritBot [18] (a commercial product), the Inductive Monitoring System (IMS) [19,20] and a one-class support vector machine (SVM) [21]. The next four subsections describe these four algorithms.…”
Section: Anomaly Detectionmentioning
confidence: 99%
“…The screening can be slow and labor intensive. Experimentation using general-purpose data-mining tools (clustering, outlier method, decision tree) [28,29] was conducted in flights STS-115 and 116. An expert systems approach was later employed to build an automatic detection tool by searching for data characteristics based on impact simulation and test experience (e.g., while an elevated G rms response can be caused by either a debris impact or mission event, the former shows up on multiple adjacent sensors and tapers off rapidly from the impacted panel, whereas the latter shows up globally across a large number of panels on both wings.…”
Section: Ascent Monitoringmentioning
confidence: 99%