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Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN
This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault ty...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232324/ https://www.ncbi.nlm.nih.gov/pubmed/34203708 http://dx.doi.org/10.3390/e23060751 |
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author | Pan, Jinghui Qu, Lili Peng, Kaixiang |
author_facet | Pan, Jinghui Qu, Lili Peng, Kaixiang |
author_sort | Pan, Jinghui |
collection | PubMed |
description | This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time. |
format | Online Article Text |
id | pubmed-8232324 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82323242021-06-26 Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN Pan, Jinghui Qu, Lili Peng, Kaixiang Entropy (Basel) Article This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time. MDPI 2021-06-15 /pmc/articles/PMC8232324/ /pubmed/34203708 http://dx.doi.org/10.3390/e23060751 Text en © 2021 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 Pan, Jinghui Qu, Lili Peng, Kaixiang Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN |
title | Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN |
title_full | Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN |
title_fullStr | Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN |
title_full_unstemmed | Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN |
title_short | Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN |
title_sort | sensor and actuator fault diagnosis for robot joint based on deep cnn |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232324/ https://www.ncbi.nlm.nih.gov/pubmed/34203708 http://dx.doi.org/10.3390/e23060751 |
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