General Purpose Data-Driven Monitoring for Space Operations

Iverson, David; Martin, Roscoe C.; Schwabacher, Mark; Spirkovska, Lilly; Taylor, William M.; Mackey, Ryan; Castle, J. P.; Baskaran, V.

doi:10.2514/1.54964

Cited by 42 publications

(18 citation statements)

References 8 publications

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“…Since the ground test data and the in-orbit data of satellites contain mostly fault-free data, fault detection methods based on unsupervised learning have been widely researched and applied. Representative unsupervised fault detection methods are one-class support vector machine (OCSVM) [17], inductive monitoring system (IMS) [18], principal component analysis (PCA) [19], Gaussian process regression (GPR) [20], long short-term memory (LSTM) [21], and so on. Although these methods use different principles to build normal models, they all have one thing in common-all normal models used to detect faults are obtained by learning from the normal historical data.…”

Section: Introductionmentioning

confidence: 99%

A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

Zhang

Yang

et al. 2021

Computational Intelligence and Neuroscience

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Timely detection and treatment of possible incipient faults in satellites will effectively reduce the damage and harm they could cause. Although much work has been done concerning fault detection problems, the related questions about satellite incipient faults are little addressed. In this paper, a new satellite incipient fault detection method was proposed by combining the ideas of deviation in unsupervised fault detection methods and classification in supervised fault detection methods. First, the proposed method uses dynamic linear discriminant analysis (LDA) to find an optimal projection vector that separates the in-orbit data from the normal historical data as much as possible. Second, under the assumption that the parameters obey a multidimensional Gaussian distribution, it applies the normal historical data and the optimal projection vector to build a normal model. Finally, it employs the noncentral F-distribution to test whether a fault has occurred. The proposed method was validated using a numerical simulation case and a real satellite fault case. The results show that the method proposed in this paper is more effective at detecting incipient faults than traditional methods.

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Section: Introductionmentioning

confidence: 99%

A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

Zhang

Yang

et al. 2021

Computational Intelligence and Neuroscience

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“…Tariq et al [4], Hundman et al [5], and Fuertes et al [6] studied on the spacecraft anomaly detection based on the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Long Short-Term Memory (LSTM) with the exhaustive testing on the telemetry of Centre National d'Etudes Spatiales (CNES) spacecraft. Iverson et al [7] and Robinson et al [8] studied on the space operation assistant based on the data-driven and model-based monitoring techniques applied in several space missions. To sum up, the applications of machine learning in space missions mainly focus on the forecasting and outlier detection in order to provide the flight control procedure with much more information about the spacecraft operation status, most of which are operated in MCC on ground without consideration of space-to-ground wireless communication limits.…”

Section: Introductionmentioning

confidence: 99%

Study on the Satellite Telemetry Data Classification Based on Self-Learning

2020

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Since great redundancy of telemetry data of spacecraft, telemetry data compression is a good solution for the limited bandwidth and contact wireless links. It is important to obtain accurate data characteristic firstly. State-of-the-art machine learning methods work well on data mining and pattern recognition under conditions of the given test data set, which could be used as the available tools for post-event data processing and analysis, such as trend forecasting and outlier detection, but they have not provided the proper solution from the source on-board. In this paper, four base classes of the telemetry data are suggested and studied through the time series feature and information entropy analysis, then a new on-board lightweight self-learning algorithm named Classification Probability calculation-Window Step optimization (CP-WS) is proposed to obtain the class features and make the decision of each single parameter from the continuous discrete telemetry time series. Simulation results show that, our algorithm correctly classifies the simulation and real mission data into the appropriate base class with advantages of high classification accuracy as 100% and adaptive computational complexity from O(L 2) to O(L), which could be used in satellite on-board data compression for space-to-ground transmission, especially for the deep space explorers to save important status with less on-board storage space.

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“…In addition, it is difficult to know how important a top-ranked discord of a certain parameter is in relation to the other parameters' top-ranked discords. D. L. Iverson and colleagues (Iverson et al 2012) present the inductive monitoring system (IMS), which uses clustering to characterize normal itera- February -April, OOL on 13 July. This thermostat has been properly working showing the same behavior for 10 years.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Telemetry Monitoring with Novelty Detection

Martínez-Heras

Donati

2014

AI Magazine

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T he most widely extended approach for automatically detecting anomalous behavior in space operations is the use of out-of-limits (OOL) alarms. The OOL approach consists of defining an upper and lower threshold so that when a measurement goes above the upper limit or below the lower one, an alarm is triggered. Then engineers will inspect the parameter that is out of limits and determine whether it is an anomaly or not and decide which action to take (for example, run a procedure). This is the original outof-limits concept.The current OOL concept has evolved to cope with more situations such as distinguishing between soft and hard limits; for example, a soft OOL triggers a warning to pay attention, a hard OOL triggers an error that demands attention. Soft limits are contained within hard limits. In addition OOL thresholds (soft and hard) can be configured so that different thresholds are applicable in different situations (for example, depending on the working mode of a given instrument).

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General Purpose Data-Driven Monitoring for Space Operations

Cited by 42 publications

References 8 publications

A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

Study on the Satellite Telemetry Data Classification Based on Self-Learning

Enhanced Telemetry Monitoring with Novelty Detection

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