Evidence map›Paper›PMID 40807880›Full record

ArticleSensors (Basel, Switzerland)2025

A Method for Evaluating the Performance of Main Bearings of TBM Based on Entropy Weight-Grey Correlation Degree.

Zhihong Sun, Yuanke Wu, Hao Xiao, Panpan Hu, Zhenyong Weng, Shunhai Xu, Wei Sun

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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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.

Zhihong SunSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.
Yuanke WuChina Railway Engineering Equipment Group Co., Ltd., Zhengzhou 450016, China.
Hao XiaoChina Railway Engineering Equipment Group Co., Ltd., Zhengzhou 450016, China.
Panpan HuChina Railway Engineering Equipment Group Co., Ltd., Zhengzhou 450016, China.
Zhenyong WengChina Railway Engineering Equipment Group Co., Ltd., Zhengzhou 450016, China.
Shunhai XuChina Railway Engineering Equipment Group Co., Ltd., Zhengzhou 450016, China.
Wei SunSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

Funding

Henan Province Science and Technology Research Project, China 242102221010
6 · The paper itself

Abstract

The main bearing of a tunnel boring machine (TBM) is a critical component of the main driving system that enables continuous excavation, and its performance is crucial for ensuring the safe operation of the TBM. Currently, there are few testing technologies for TBM main bearings, and a comprehensive testing and evaluation system has yet to be established. This study presents an experimental investigation using a self-developed, full-scale TBM main bearing test bench. Based on a representative load spectrum, both operational condition tests and life cycle tests are conducted alternately, during which the signals of the main bearing are collected. The observed vibration signals are weak, with significant vibration attenuation occurring in the large structural components. Compared with the test bearing, which reaches a vibration amplitude of 10 g in scale tests, the difference is several orders of magnitude smaller. To effectively utilize the selected evaluation indicators, the entropy weight method is employed to assign weights to the indicators, and a comprehensive analysis is conducted using grey relational analysis. This strategy results in the development of a comprehensive evaluation method based on entropy weighting and grey relational analysis. The main bearing performance is evaluated under various working conditions and the same working conditions in different time periods. The results show that the greater the bearing load, the lower the comprehensive evaluation coefficient of bearing performance. A multistage evaluation method is adopted to evaluate the performance and condition of the main bearing across multiple working scenarios. With the increase of the test duration, the bearing performance exhibits gradual degradation, aligning with the expected outcomes. The findings demonstrate that the proposed performance evaluation method can effectively and accurately evaluate the performance of TBM main bearings, providing theoretical and technical support for the safe operation of TBMs.

Indexed as

experimental testmain bearingperformance evaluationTBM

Identifiers

PMID40807880
PMCPMC12349183

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