Evidence map›Paper›PMID 40292838›Full record

ReviewSensors (Basel, Switzerland)2025

Fault Types and Diagnostic Methods of Manipulator Robots: A Review.

Yuepeng Zhang, Jun Wu, Bo Gao, Linzhong Xia, Chen Lu, Hui Wang, Guangzhong Cao

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Yuepeng ZhangSchool of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, China.ORCID 0000-0001-9178-1372
Jun WuSchool of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, China.
Bo GaoSchool of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, China.
Linzhong XiaSchool of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, China.ORCID 0009-0006-4132-5954
Chen LuInovance Industrial Robot Reliability Technology Research Institute, Shenzhen Institute of Information Technology, Shenzhen 518172, China.ORCID 0009-0001-1327-4196
Hui WangInovance Industrial Robot Reliability Technology Research Institute, Shenzhen Institute of Information Technology, Shenzhen 518172, China.
Guangzhong CaoGuangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-5595-7155

Funding

National Natural Science Foundation of China 92467204Projects of Shenzhen Institute of Information Technology SZIIT2025KJ021 and SZIIT2025KJ022
6 · The paper itself

Abstract

Manipulator robots hold significant importance for the development of intelligent manufacturing and industrial transformation. Manufacturers and users are increasingly focusing on fault diagnosis for manipulator robots. The voltage, current, speed, torque, and vibration signals of manipulator robots are often used to explore the fault characteristics from a frequency perspective, and temperature and sound are also used to represent the fault information of manipulator robots from different perspectives. Technically, manipulator robot fault diagnosis involving human intervention is gradually being replaced by new technologies, such as expert experience, artificial intelligence, and digital twin methods. Previous reviews have tended to focus on a single type of fault, such as analysis of reducers or joint bearings, which has led to a lack of comprehensive summary of various methods for manipulator robot fault diagnosis. Considering the needs of future research, a review of different fault types and diagnostic methods of manipulator robots provides readers with a clearer reading experience and reveals potential challenges and opportunities. Such a review helps new researchers entering the field avoid duplicating past work and provides a comprehensive overview, guiding and encouraging readers to commit to enhancing the effectiveness and practicality of manipulator robot fault diagnosis technologies.

Indexed as

fault diagnosismanipulator robotsignal acquisitiontype of fault

Identifiers

PMID40292838
PMCPMC11946664

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.