Evidence map›Paper›PMID 37960517›Full record

ArticleSensors (Basel, Switzerland)2023

Modelling of Gyratory Crusher Liner Wear Using a Digital Wireless Sensor.

Tao Ou, Wei Chen

Abstract read
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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Tao OuSchool of Mechanical and Vehicle Engineering, Hunan University, Changsha 410012, China.
Wei ChenSchool of Intelligent Manufacturing Ecosystem, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A gyratory crusher is a key mineral processing asset in a comminution circuit. Monitoring and predicting the crusher liner wear is essential to ensure the throughput and product quality are maintained during production. This study developed a digital sensor and a discrete element modelling (DEM)-coupled methodology to monitor and reconstruct the gyratory crusher concave liner wear pattern. The developed digital sensor was able to track and report the live thickness of the specific installation point on a concave liner during operation. A wear reconstruction model was then developed based on the wear intensity obtained using the DEM and digital sensor results. The wear reconstruction model predictive results were subsequently compared with site measurements after 95 days of operation. The results indicated that the wear reconstruction model showed good agreement with measured results in terms of wear zone distribution as well as quantitative wear rate prediction. The outcome of this study can be potentially utilised in the mineral processing industry for plant monitoring and automation.

Indexed as

discrete element modellinggyratory crusherintrusive sensorwear measurement

Identifiers

PMID37960517
PMCPMC10647848

What OpenQuestion holds

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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.