{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:22:40Z","timestamp":1760710960287,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2020YFB1709702","62073313"],"award-info":[{"award-number":["2020YFB1709702","62073313"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020YFB1709702","62073313"],"award-info":[{"award-number":["2020YFB1709702","62073313"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach\u2019s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.<\/jats:p>","DOI":"10.3390\/e24070871","type":"journal-article","created":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T10:39:13Z","timestamp":1656153553000},"page":"871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data"],"prefix":"10.3390","volume":"24","author":[{"given":"Yue","family":"Sun","sequence":"first","affiliation":[{"name":"Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aidong","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiufang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6134-7281","authenticated-orcid":false,"given":"Haifeng","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Liaoning Institute of Science and Technology, Benxi 117004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojia","family":"Han","sequence":"additional","affiliation":[{"name":"Intelligent Robot Research Center of Zhejiang Laboratory, Hangzhou 311100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"ref_1","first-page":"460","article-title":"An Industrial Robot Health Assessment Method for Intelligent Manufacturing","volume":"42","author":"Zhao","year":"2020","journal-title":"Robot"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/TII.2019.2912809","article-title":"Data-Driven Gearbox Failure Detection in Industrial Robots","volume":"16","author":"Vallachira","year":"2020","journal-title":"IEEE Trans. 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