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A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning

Carrying out status monitoring and fault-diagnosis research on cutter-wear status is of great significance for real-time understanding of the health status of Tunnel Boring Machine (TBM) equipment and reducing downtime losses. In this work, we proposed a new method to diagnose the abnormal wear stat...

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Detalles Bibliográficos
Autores principales: Pu, Xiaobo, Jia, Lingxu, Shang, Kedong, Chen, Lei, Yang, Tingting, Chen, Liangwu, Gao, Libin, Qian, Linmao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459918/
https://www.ncbi.nlm.nih.gov/pubmed/36081145
http://dx.doi.org/10.3390/s22176686
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author Pu, Xiaobo
Jia, Lingxu
Shang, Kedong
Chen, Lei
Yang, Tingting
Chen, Liangwu
Gao, Libin
Qian, Linmao
author_facet Pu, Xiaobo
Jia, Lingxu
Shang, Kedong
Chen, Lei
Yang, Tingting
Chen, Liangwu
Gao, Libin
Qian, Linmao
author_sort Pu, Xiaobo
collection PubMed
description Carrying out status monitoring and fault-diagnosis research on cutter-wear status is of great significance for real-time understanding of the health status of Tunnel Boring Machine (TBM) equipment and reducing downtime losses. In this work, we proposed a new method to diagnose the abnormal wear state of the disc cutter by using brain-like artificial intelligence to process and analyze the vibration signal in the dynamic contact between the disc cutter and the rock. This method is mainly aimed at realizing the diagnosis and identification of the abnormal wear state of the cutter, and is not aimed at the accurate measurement of the wear amount. The author believes that when the TBM is operating at full power, the cutting forces are very high and the rock is successively broken, resulting in a complex circumstance, which is inconvenient to vibration signal acquisition and transmission. If only a small thrust is applied, to make the cutters just contact with the rock (less penetration), then the cutters will run more smoothly and suffer less environmental interference, which would be beneficial to apply the method proposed in this paper to detect the state of the cutters. A specific example was to use the frequency-domain characteristics of the periodic vibration waveform during the contact between the cutter and the granite to identify the wear status (including normal wear state, wear failure state, angled wear failure state) of the disc cutter through the artificial neural network, and the diagnosis accuracy rate is 90%.
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spelling pubmed-94599182022-09-10 A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning Pu, Xiaobo Jia, Lingxu Shang, Kedong Chen, Lei Yang, Tingting Chen, Liangwu Gao, Libin Qian, Linmao Sensors (Basel) Article Carrying out status monitoring and fault-diagnosis research on cutter-wear status is of great significance for real-time understanding of the health status of Tunnel Boring Machine (TBM) equipment and reducing downtime losses. In this work, we proposed a new method to diagnose the abnormal wear state of the disc cutter by using brain-like artificial intelligence to process and analyze the vibration signal in the dynamic contact between the disc cutter and the rock. This method is mainly aimed at realizing the diagnosis and identification of the abnormal wear state of the cutter, and is not aimed at the accurate measurement of the wear amount. The author believes that when the TBM is operating at full power, the cutting forces are very high and the rock is successively broken, resulting in a complex circumstance, which is inconvenient to vibration signal acquisition and transmission. If only a small thrust is applied, to make the cutters just contact with the rock (less penetration), then the cutters will run more smoothly and suffer less environmental interference, which would be beneficial to apply the method proposed in this paper to detect the state of the cutters. A specific example was to use the frequency-domain characteristics of the periodic vibration waveform during the contact between the cutter and the granite to identify the wear status (including normal wear state, wear failure state, angled wear failure state) of the disc cutter through the artificial neural network, and the diagnosis accuracy rate is 90%. MDPI 2022-09-04 /pmc/articles/PMC9459918/ /pubmed/36081145 http://dx.doi.org/10.3390/s22176686 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pu, Xiaobo
Jia, Lingxu
Shang, Kedong
Chen, Lei
Yang, Tingting
Chen, Liangwu
Gao, Libin
Qian, Linmao
A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning
title A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning
title_full A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning
title_fullStr A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning
title_full_unstemmed A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning
title_short A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning
title_sort new strategy for disc cutter wear status perception using vibration detection and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459918/
https://www.ncbi.nlm.nih.gov/pubmed/36081145
http://dx.doi.org/10.3390/s22176686
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