An Analytical Investigation of Anomaly Detection Methods Based on Sequence to Sequence Model in Satellite Power Subsystem.

Weihua Jin, Shijie Zhang, Bo Sun, Pengli Jin, Zhidong Li

Open access · goldAbstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

1.1field-weighted citation impact, top 19% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 10 citations in OpenAlex.

TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System.Sensors (Basel, Switzerland) · 2022
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Weihua JinResearch Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, China.ORCID 0000-0002-8720-9274

Shijie ZhangResearch Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, China.

Bo SunBeijing Institute of Spacecraft System Engineering, Beijing 100094, China.

Pengli JinSchool of Materials Science & Engineering, Harbin Institute of Technology, Harbin 150080, China.

Zhidong LiBeijing Institute of Spacecraft System Engineering, Beijing 100094, China.

Harbin Institute of Technology · CNChina Academy of Space Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The satellite power subsystem is responsible for all power supply in a satellite, and is an important component of it. The system's performance has a direct impact on the operations of other systems as well as the satellite's lifespan. Sequence to sequence (seq2seq) learning has recently advanced, gaining even more power in evaluating complicated and large-scale data. The potential of the seq2seq model in detecting anomalies in the satellite power subsystem is investigated in this work. A seq2seq-based scheme is given, with a thorough comparison of different neural-network cell types and levels of data smoothness. Three specific approaches were created to evaluate the seq2seq model performance, taking into account the unsupervised learning mechanism. The findings reveal that a CNN-based seq2seq with attention model under suitable data-smoothing conditions has a better ability to detect anomalies in the satellite power subsystem.

Indexed as

anomaly detectionsatellite power subsystemsequence to sequence

Identifiers

PMID35270969

DOI10.3390/s22051819

PMCPMC8915031

OpenAlexW4214763078

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.