Machine Learning for Rocket Propulsion Health Monitoring

Schwabacher, Mark

doi:10.4271/2005-01-3370

Cited by 27 publications

(28 citation statements)

References 5 publications

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“…Two unsupervised anomaly detection algorithms implemented by Orca and GritBot demonstrate these methods, respectively. Detailed results of the application of these algorithms have been previously published by Schwabacher [24]. Furthermore, Tumer and Agogino [1] have presented an information theoretic entropy-based algorithm for anomaly detection in SSME data, and Fiorucci et.…”

Section: Introductionmentioning

confidence: 95%

Unsupervised Anomaly Detection and Diagnosis for Liquid Rocket Engine Propulsion

Martin

2007

2007 IEEE Aerospace Conference

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The results of a comprehensive array of unsupervised anomaly detection algorithms applied to Space Shuttle Main Engine (SSME) data are presented. Most of the algorithms are based upon variants of the well-known unconditional Gaussian mixture model (GMM). One goal ofthe paper is to demonstrate the maximum utility of these algorithms by the exhaustive development of a very simple GMM. Selected variants will provide us with the added benefit of diagnostic capability.Another algorithm that shares a common technique for detection with the GMM is presented, but instead uses a different modeling paradigm. The model provides a more rich description of the dynamics of the data, however the data requirements are quite modest. We will show that this very simple and straightforward method finds an event that characterizes a departure from nominal operation. We show that further diagnostic investigation with the GMM-based method can be used as a means to gain insight into operational idiosyncrasies for this nominally categorized test. Therefore, by using both modeling paradigms we can corroborate planned operational commands or provide warnings for unexpected operational commands.

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Section: Introductionmentioning

confidence: 95%

Unsupervised Anomaly Detection and Diagnosis for Liquid Rocket Engine Propulsion

Martin

2007

2007 IEEE Aerospace Conference

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“…The algorithm is tested successfully on the data collected during mission STS-107 of the Columbia space shuttle, which exploded during reentry because of a breach in its thermal protection system [Geh03]. An approach very similar to the one just introduced is presented in [Sch05]. In this work, an unsupervised detection algorithm named Orca, developed by the authors on the basis of the nearest-neighbor approach, is applied to the test data of the space shuttle main engine and of a rocket engine stand.…”

Section: Data-driven Approachesmentioning

confidence: 99%

Artificial Intelligence for Space Applications

Girimonte

Izzo

2007

Intelligent Computing Everywhere

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Summary. The ambitious short-term and long-term goals set down by the various national space agencies call for radical advances in several of the main space engineering areas, the design of intelligent space agents certainly being one of them. In recent years, this has led to an increasing interest in artificial intelligence by the entire aerospace community. However, in the current state of the art, several open issues and showstoppers can be identified. In this chapter, we review applications of artificial intelligence in the field of space engineering and space technology and identify open research questions and challenges. In particular, the following topics are identified and discussed: distributed artificial intelligence, enhanced situation self-awareness, and decision support for spacecraft system design.

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“…It uses a modified pruning rule that allows for increased computational efficiency, running in near linear time as the number of "top score" outlier points selected for evaluation decreases. More information on this algorithm and some of its applications can be found in previous work (Bay and Schwabacher, 2003); (Schwabacher, 2005). This algorithm outputs a total score which represents the average distance to the nearest k neighbors in the multi-dimensional feature space containing all of the variables.…”

Section: Orcamentioning

confidence: 99%

Evaluation of Anomaly Detection Capability for Ground-Based Pre-Launch Shuttle Operations

Martin

2010

Aerospace Technologies Advancements

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Machine Learning for Rocket Propulsion Health Monitoring

Cited by 27 publications

References 5 publications

Unsupervised Anomaly Detection and Diagnosis for Liquid Rocket Engine Propulsion

Unsupervised Anomaly Detection and Diagnosis for Liquid Rocket Engine Propulsion

Artificial Intelligence for Space Applications

Evaluation of Anomaly Detection Capability for Ground-Based Pre-Launch Shuttle Operations

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