Evidence map›Paper›PMID 40126398›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Symmetrical Triboelectric In Situ Self-Powered Sensing and Fault Diagnosis for Double-Row Tapered Roller Bearings in Wind Turbines: An Integrated and Real-Time Approach.

Song Wang, Xiantao Zhang, Tenghao Ma, Yun Kong, Shuai Gao, Qinkai Han

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. 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. Article
  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

6 authors.

Song WangState Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.
Xiantao ZhangState Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.
Tenghao MaState Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.
Yun KongSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Shuai GaoState Key Laboratory of Mechanical Transmission, College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China.
Qinkai HanState Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.ORCID https://orcid.org/0000-0002-3625-2694

Funding

Beijing Natural Science Foundation 3252008National Natural Science Foundation of China 12272199National Natural Science Foundation of China 12402057National Natural Science Foundation of China 52405090Young Elite Scientists Sponsorship Program by CAST 2023QNRC001
6 · The paper itself

Abstract

Double-row tapered roller bearings (DTRBs) are widely used in wind turbines because of their high load-bearing capacity and durability. However, wind turbines typically operate in harsh environments, subjecting bearings to complex working conditions, which significantly increases the difficulty of operational status monitoring. Traditional monitoring methods rely on external power sources and complex sensor networks, which make them susceptible to environmental interference, and complicated to maintain. This paper presents an innovative, integrated symmetrical single-electrode triboelectric double-row tapered roller bearing (SST-DTRB) by incorporating a triboelectric nanogenerator (TENG) with DTRB. This scheme converts the frictional energy generated during bearing operation into electrical output, producing signals that enable simultaneous sensing of both ends of DTRB. Experimental results demonstrate that this monitoring scheme exhibits high sensitivity, stability, and reliability, with excellent robustness in material selection and design gap, and is capable of long-term operation without external power sources. The effectiveness and self-sensing capability of SST-DTRB under variable speeds are validated using a wind turbine test bench. High-accuracy bearing fault diagnosis under multiple conditions is achieved based on time-frequency transformation and deep residual neural networks. The proposed SST-DTRB provides in situ self-powered sensing capability for wind turbines and offers new insights in the development of intelligent sensing systems.

Indexed as

fault diagnosisin situ self‐powered sensingtriboelectric nanogeneratorwind turbine

Identifiers

PMID40126398
PMCPMC12097082

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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.