Real-time change detection in data streams with FPGAs

Vega, J.; Dormido-Canto, S.; Cruz, Tomás; Ruiz, M.; Barrera, E.; Castro, R.; Murari, A.; Ochando, M. A.

doi:10.1016/j.fusengdes.2013.12.049

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How To Detect Off-normal Behavior1

Introduction1

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Cited by 4 publications

(2 citation statements)

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“…To this end, a model can be built by using only signals where the target off-normal behavior occurs. Hence, during a pulse we can detect the specific behavior in real time such as it is done in [7].…”

Section: How To Detect Off-normal Behaviormentioning

confidence: 99%

Initial results with time series forecasting of TJ-II heliac waveforms

Farías

Dormido-Canto

Vega³

et al. 2015

Fusion Engineering and Design

Self Cite

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Section: How To Detect Off-normal Behaviormentioning

confidence: 99%

Initial results with time series forecasting of TJ-II heliac waveforms

Farías

Dormido-Canto

Vega³

et al. 2015

Fusion Engineering and Design

Self Cite

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“…Maybe, some quantitative or qualitative analysis methods [4][5][6], which are based on data mining and knowledge discovery technologies, are usable to explore the inherent regularity or characteristic information, and some fault detection and diagnosis algorithms [7][8][9][10][11], which are based on the threshold monitoring as well as data driven detection, may be directly realized from telemetry data. But, there were few studies discussed the results about inherent relevance between the homologous data abnormal changes in a multidimensional telemetry data flow, which may be give us some valuable inspiration where the faults arise and which kind of faults result in abnormal changes in part of the multidimensional telemetry data flow.…”

Section: Introductionmentioning

confidence: 99%

Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit

Shou-song¹

2017

IJDST

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Abstract:To explore the conjunction of abnormal changes among different processes is a key and challenging technique problem in processes monitoring, in faults analysis, and in faults location. In this paper, an indication series is used to symbolize the abnormal change in sampling series, two kinds of conjunction test indices are constructed to measure the conjunction degrees, which rely on the indication series of multidimensional synchronization sampling series and the abnormal change percentage series of multidimensional asynchronous sampling series separately. What is more, these conjunction-test indices are successfully used to set up the clustering algorithms of abnormal changes in multidimensional series. Some Monte Carlo results show that algorithms given in this paper are efficient. The idea and technological methods of this paper are helpful for us to get viable approaches to analyze abnormal changes and to diagnose faults in large-scale dynamic system.

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