{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T02:05:03Z","timestamp":1769738703203,"version":"3.49.0"},"reference-count":22,"publisher":"Cambridge University Press (CUP)","issue":"12","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Aiming at the problem of low accuracy of robot joint fault diagnosis, a fault diagnosis method of robot joint based on BP neural network is designed. In this paper, the UR10 robot is taken as the research object, and the end pose data of the robot are collected in real time. By injecting different joint errors and changing the sampling frequency, the joint fault database is collected and established, and the BP neural network is used for training to obtain the robot neural network fault diagnosis model. The fault diagnosis model can output the joint fault of the input end pose data. And we analyzed the influence of different joint angle errors and different training sets on the accuracy of joint fault diagnosis of the robot. The results show that when the sampling frequency is 250 Hz, the simulation result of joint fault diagnosis accuracy with the fault degree of 0.5\u00b0 is 99.17%, and the experimental result is 97.87%. Compared with traditional data-driven methods, it has higher accuracy and diagnostic efficiency, and compared with existing machine learning methods, it also achieves a high accuracy while reducing the network complexity. The effectiveness of the BP neural network robot joint fault diagnosis method is verified by experiments.<\/jats:p>","DOI":"10.1017\/s0263574722000984","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T07:53:11Z","timestamp":1658476391000},"page":"4388-4404","source":"Crossref","is-referenced-by-count":10,"title":["Fault diagnosis of robot joint based on BP neural network"],"prefix":"10.1017","volume":"40","author":[{"given":"Ming","family":"Hu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6026-4521","authenticated-orcid":false,"given":"Jianguo","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4990-8517","authenticated-orcid":false,"given":"Jing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Lijian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"S0263574722000984_ref8","doi-asserted-by":"publisher","DOI":"10.1108\/IJQRM-03-2020-0069"},{"key":"S0263574722000984_ref12","first-page":"25","article-title":"Observer-based fault diagnosis scheme for actuators in robot manipulators","volume":"39","author":"Huang","year":"2020","journal-title":"Meas. Control Technol."},{"key":"S0263574722000984_ref15","doi-asserted-by":"crossref","unstructured":"[15] Hong, Y. , Sun, Y. , Zou, X. and Long, J. , \u201cMulti-joint Industrial Robot Fault Identification Using Deep Sparse Auto-Encoder Network with Attitude Data,\u201d In: 2020 Prognostics and Health Management Conference (PHM-Besan\u00e7on) , Besancon, France (2020) pp. 176\u2013179.","DOI":"10.1109\/PHM-Besancon49106.2020.00036"},{"key":"S0263574722000984_ref3","first-page":"095440542097051","article-title":"Fault diagnosis of flexible production line machining center based on PCA and ABC-LVQ","volume":"235","author":"Xia","year":"2020","journal-title":"Proc. Inst. Mech. Eng. B J. Eng. 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