Evidence map›Paper›PMID 39796971›Full record

ArticleSensors (Basel, Switzerland)2024

Machine Condition Monitoring System Based on Edge Computing Technology.

Igor Halenar, Lenka Halenarova, Pavol Tanuska, Pavel Vazan

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Igor HalenarInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 812 43 Bratislava, Slovakia.
Lenka HalenarovaInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 812 43 Bratislava, Slovakia.
Pavol TanuskaInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 812 43 Bratislava, Slovakia.ORCID 0000-0001-7025-1911
Pavel VazanInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 812 43 Bratislava, Slovakia.

Funding

Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences VEGA 1/0193/22
6 · The paper itself

Abstract

The core of this publication is the design of a system for evaluating the condition of production equipment and machines by monitoring selected parameters of the production process with an additional sensor subsystem. The main positive of the design is the processing of data from the sensor layer using artificial intelligence (AI) and expert systems (ESs) with the use of edge computing (EC). Sensor information is processed directly at the sensor level on the monitored equipment, and the results of the individual subsystems are stored in the form of triggers in a database for use in the predictive maintenance process. The whole solution includes the design of suitable sensors and of the implementation of the sensor layer, the description of data processing algorithms, the design on the communication infrastructure for the whole system, and tests in the form of experimental operation of the device in laboratory conditions. The solution includes the visualisation of the production system status for the operator using an interactive online map.

Indexed as

edge computingmonitoringpredictive maintenancesensorssystem

Identifiers

PMID39796971
PMCPMC11723020

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.