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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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%. |
format | Online Article Text |
id | pubmed-9459918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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