{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:46:01Z","timestamp":1785512761174,"version":"3.56.0"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB1309900"],"award-info":[{"award-number":["2019YFB1309900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e23060751","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T11:00:33Z","timestamp":1623754833000},"page":"751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN"],"prefix":"10.3390","volume":"23","author":[{"given":"Jinghui","family":"Pan","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Foshan University, Foshan 528231, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixiang","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26754","DOI":"10.1109\/ACCESS.2017.2773127","article-title":"Working together: A review on safe human-robot collaboration in industrial environments","volume":"5","author":"Becerra","year":"2017","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5204","DOI":"10.1109\/TII.2019.2900119","article-title":"A sensorless hand guiding scheme based on model identification and control for industrial robot","volume":"15","author":"Zhang","year":"2019","journal-title":"IEEE Trans. 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