Evidence map›Paper›PMID 30678151›Full record

ArticleSensors (Basel, Switzerland)2019

An Approach for Managing Manufacturing Assets through Radio Frequency Energy Harvesting.

Muhammad Ashhal Tahir, Borja Ramis Ferrer, Jose Luis Martinez Lastra

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

Muhammad Ashhal TahirFuture and Automation Systems and Technologies Laboratory (FAST-Lab.), Tampere University, P.O. Box 600, FI-33014 Tampere, Finland. ashaltahir@gmail.com.
Borja Ramis FerrerFuture and Automation Systems and Technologies Laboratory (FAST-Lab.), Tampere University, P.O. Box 600, FI-33014 Tampere, Finland. borja.ramisferrer@tuni.fi.ORCID 0000-0002-0525-163X
Jose Luis Martinez LastraFuture and Automation Systems and Technologies Laboratory (FAST-Lab.), Tampere University, P.O. Box 600, FI-33014 Tampere, Finland. jose.martinezlastra@tuni.fi.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The manufacturing industry requests novel solutions that will permit enterprises to stay competitive in the market. This leads to decisions being made based on different technologies that are focused on real-time accurate measurement and monitoring of manufacturing assets. In the context of traceability, radio frequency identification (RFID) tags have been traditionally used for tracking, monitoring, and collecting data of various manufacturing resources operating along the value chain. RFID tags and microelectromechanical systems (MEMS) sensors enable the monitoring of manufacturing assets by providing real-time data. Such devices are usually powered by batteries that need regular maintenance, which in turn leads to delays that affect the overall manufacturing process time. This article presents a low-cost approach to detect and measure radio frequency (RF) signals in assembly lines for optimizing the manufacturing operations in the manufacturing industry. Through the detection and measurement of RF signals, the RF energy can be harvested at certain locations on the assembly line. Then, the harvested energy can be supplied to the MEMS sensors, minimizing the regular maintenance for checking and replacing batteries. This leads to an increase in the operational efficiency and an overall reduction in operational and maintenance costs.

Indexed as

industrial automationmanufacturing systemsradio frequency energy harvestingwireless MEMS sensors

Identifiers

PMID30678151
PMCPMC6387058

What OpenQuestion holds

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LicenceCC BY
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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.