Evidence map›Paper›PMID 42279964›Full record

ArticleMaterials (Basel, Switzerland)2026

Study on the Characteristics of MBN and MAE Signals in P92 Steel.

Ziyi Huang, Xiaochu Pang, Xinnan Zheng, Saibo She, Xufei Liu, Wuliang Yin, Lisha Peng

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2026. 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

7 authors.

Ziyi HuangDepartment of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK.ORCID 0009-0006-8567-8292
Xiaochu PangDepartment of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK.ORCID 0009-0005-3349-4320
Xinnan ZhengDepartment of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK.ORCID 0000-0002-4229-4188
Saibo SheDepartment of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK.
Xufei LiuDepartment of Electrical Engineering, Tsinghua University, Beijing 100084, China.
Wuliang YinDepartment of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK.ORCID 0000-0001-5927-3052
Lisha PengDepartment of Electrical Engineering, Tsinghua University, Beijing 100084, China.ORCID 0000-0002-7315-2692

Funding

National Natural Science Foundation of China 52477008
6 · The paper itself

Abstract

The demand for efficient combustion in boilers drives the development of ultra-supercritical power plants. P92 steel pressure, and pipelines operate in high-temperature and high-pressure environments and are prone to high-temperature creep damage. Non-destructive testing is a key method to ensure the safety of the pipe. However, existing non-destructive testing methods are difficult to achieve non-destructive detection of creep damage. Creep damage affects magnetic Barkhausen noise (MBN) and magneto-acoustic emission (MAE) signals; therefore, it is possible to evaluate creep damage using these signals. This article first establishes a theoretical model for MBN and MAE. Afterward, the influence of magnetizing waveform, amplitude, and frequency on MBN and MAE signals was studied through experiments. Finally, by analyzing the characteristics of MBN and MAE signals, the optimal magnetization conditions and signal characteristic parameters for detecting creep damage using MBN and MAE signals were determined. The experimental results also confirmed the correctness of the theoretical model.

Indexed as

creep damage evaluationmagnetic Barkhausen noisemagneto-acoustic emission

Identifiers

PMID42279964
PMCPMC13257781

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LicenceCC BY
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Registered trials

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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.