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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...

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Detalles Bibliográficos
Autores principales: Pan, Jinghui, Qu, Lili, Peng, Kaixiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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