Adaptive limit checking for spacecraft telemetry data using regression tree learning

Yairi, Takehisa; Nakatsugawa, Munehide; Hori, Koichi; Nakasuka, S.; Machida, Kazuo; Ishihama, Naoki

doi:10.1109/icsmc.2004.1401008

Cited by 14 publications

(10 citation statements)

References 2 publications

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“…To explore potential improvements over OOL approaches, data-driven methods are developed by learning knowledge from data using various machine learning approaches. These approaches include dimensionality reduction approaches [7,22,24], clustering-based approaches [8,[23][24][25], and nearest neighbor methods [26][27][28].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Wang

Gong

Zhang

et al. 2022

International Journal of Aerospace Engineering

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Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Wang

Gong

Zhang

et al. 2022

International Journal of Aerospace Engineering

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“…The cost function J i of WS optimization for i-th base class (i = 1, 2, 3, 4 indicates stationary class, linear class, cyclic class, and general non-linear class respectively) is shown in equation (3).…”

Section: ) Optimization Objectivementioning

confidence: 99%

“…• Cyclic data: Sine function data ranging in [1,3] with 500 points, as shown in Figure 5(b), which could be deemed as the available voltages of some equipment on-board.…”

Section: A Simulation Data 1) Data Generationmentioning

confidence: 99%

“…As a fast-developing technology in recent years, machine learning (ML) related technologies have been studied widely in space missions with telemetry in recent years. Yairi et al [1]- [3] studied on the satellite health monitoring based on the probabilistic clustering, dimensionality reduction, hidden markov, regression tree. 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.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The method is completely data driven as opposed to other studies which require prior knowledge. 5,6 The remainder of this paper is organized as follows. In Section II, we describe the spacecraft SOH data repository.…”

Section: Introductionmentioning

confidence: 99%

Spacecraft State-of-health (SOH) Analysis via Data Mining

Lindsay

Woodbridge

2014

SpaceOps 2014 Conference

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Spacecraft state-of-health (SOH) analysis typically consists of limit-checking to compare incoming measurand values against their predetermined limits. While useful, this approach requires significant engineering insight along with the ability to evolve limit values over time as components degrade and their operating environment changes. In addition, it fails to take into account the effects of measurand combinations, as multiple values together could signify an imminent problem. A more powerful approach is to apply data mining techniques to uncover hidden trends and patterns as well as interactions among groups of measurands. In an internal research and development effort, software engineers at Sandia National Laboratories explored ways to mine SOH data from a remote sensing spacecraft. Because our spacecraft uses variable sample rates and packetized telemetry to transmit values for 30,000 measurands across 700 unique packet IDs, our data is characterized by a wide disparity of time and value pairs. We discuss how we summarized and aligned this data to be efficiently applied to data mining algorithms. We apply supervised learning including decision tree and principal component analysis and unsupervised learning including kmeans and orthogonal partitioning clustering and one-class support vector machine to four different spacecraft SOH scenarios after the data preprocessing step. Our experiment results show that data mining is a very good low-cost and high-payoff approach to SOH analysis and provides an excellent way to exploit vast quantities of time-series data among groups of measurands in different scenarios. Our scenarios show that the supervised cases were particularly useful in identifying key contributors to anomalous events, and the unsupervised cases were well-suited for automated analysis of the system as a whole. The developed underlying models can be updated over time to accurately represent a changing operating environment and ultimately to extend the mission lifetime of our valuable space assets.

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Adaptive limit checking for spacecraft telemetry data using regression tree learning

Cited by 14 publications

References 2 publications

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

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

Spacecraft State-of-health (SOH) Analysis via Data Mining

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