Evidence map›Paper›PMID 37050710›Full record

ArticleSensors (Basel, Switzerland)2023

Novel Fault Diagnosis of a Conveyor Belt Mis-Tracking via Motor Current Signature Analysis.

Mohamed Habib Farhat, Len Gelman, Abdulmumeen Onimisi Abdullahi, Andrew Ball, Gerard Conaghan, Winston Kluis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

6 authors.

Mohamed Habib FarhatSchool of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.ORCID 0000-0003-0406-3706
Len GelmanSchool of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
Abdulmumeen Onimisi AbdullahiSchool of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
Andrew BallSchool of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.ORCID 0000-0002-8593-6830
Gerard ConaghanDaifuku Airport Technologies, Sutton Road, Hull HU7 0DR, UK.ORCID 0000-0003-1158-8607
Winston KluisBabcock International Group, Schiphol Boulevard 363, 1118 BJ Schiphol, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For the first time ever worldwide, this paper proposes, investigates, and validates, by multiple experiments, a new online automatic diagnostic technology for the belt mis-tracking of belt conveyor systems based on motor current signature analysis (MCSA). Three diagnostic technologies were investigated, experimentally evaluated, and compared for conveyor belt mis-tracking diagnosis. The proposed technologies are based on three higher-order spectral diagnostic features: bicoherence, tricoherence, and the cross-correlation of spectral moduli of order 3 (CCSM3). The investigation of the proposed technologies via comprehensive experiments has shown that technology based on the CCSM3 is highly effective for diagnosing a conveyor belt mis-tracking via MCSA.

Indexed as

conveyor beltdata analysisdiagnosis

Identifiers

PMID37050710
PMCPMC10099345

What OpenQuestion holds

Textmetadata
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.