Analysis of Space Shuttle main engine data using beacon-based exception analysis for multi-missions

Park, H.; Mackey, Ryan; James, Mark; Zak, Michail; Kynard, Michael H.; Sebghati, J.; Greene, W. Dale

doi:10.1109/aero.2002.1036123

Cited by 30 publications

(21 citation statements)

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“…Park et al applied the BEAM (Beacon-based Exception Analysis for Multi-Missions) system to anomaly detection in SSME data [30]. BEAM has nine components that use nine different approaches to anomaly detection.…”

Section: Related Workmentioning

confidence: 98%

Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring

Schwabacher

Oza

Matthews

2009

Journal of Aerospace Computing, Information, and Communication

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This article describes the results of applying four unsupervised anomaly detection algorithms to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms.

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Section: Related Workmentioning

confidence: 98%

Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring

Schwabacher

Oza

Matthews

2009

Journal of Aerospace Computing, Information, and Communication

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“…Methods to detect anomalies without using abnormal signals have also been proposed for various machines such as space crafts [4] [5], aircrafts [6], space shuttles [7] [8], bearings and couplings of rotating machines [9], and turbine rotors [10]. These methods learn rules that capture the normal behavior or a stochastic model of the normal signals.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection of motors with feature emphasis using only normal sounds

Ono

Onishi

Koshinaka

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper proposes an anomaly detection method for sound signals observed from motors in operation without using abnormal signals. It is based on feature emphasis and effectively detects anomalies that appear in a small subset of features. To emphasize the features, the method optimally estimates the contribution rates of various features to the dissimilarity score between an observed signal and the distribution of normal signals. We report here our evaluation of the method using sound data observed from PCs and fans in operation. The evaluation demonstrates that the proposed method emphasizes a small subset of narrow frequency ranges of sounds and that it achieves an error reduction rate of up to 76%.

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“…Park, et al applied the BEAM (Beacon-based Exception Analysis for Multi-Missions) system to anomaly detection in SSME data [13]. BEAM has nine components that use nine different approaches to anomaly detection.…”

Section: Related Workmentioning

confidence: 99%

“…BEAM has nine components that use nine different approaches to anomaly detection. The work reported in [13] only used one of the nine components: the Dynamical Invariant Anomaly Detector (DIAD). DIAD is an unsupervised anomaly detection algorithm, like GritBot and Orca.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for Rocket Propulsion Health Monitoring

Schwabacher¹

2005

SAE Technical Paper Series

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This paper describes the initial results of applying two machine-learning-based unsupervised anomaly detection algorithms, Orca and GritBot, to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms.

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Analysis of Space Shuttle main engine data using beacon-based exception analysis for multi-missions

Cited by 30 publications

References 0 publications

Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring

Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring

Anomaly detection of motors with feature emphasis using only normal sounds

Machine Learning for Rocket Propulsion Health Monitoring

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