{"title":"利用多重深度网络和多核支持向量数据描述预测工业机器人 RV 减速器的剩余使用寿命","authors":"Guoai Ren, Zhihai Wang, Xiaoqin Liu, Feng Song","doi":"10.1007/s12206-024-0703-y","DOIUrl":null,"url":null,"abstract":"<p>The remaining useful life prediction of Industrial robot RV reducer is challenging due to the strong redundancy, unstable degradation initiation point, and environmental interference that may obscure the key state information during long-term operation. To address this problem, this paper proposes a novel remaining useful life prediction method for robot RV reducer with multi-depth network and multi-kernel support vector data description. Firstly, the degradation features are constructed by the hidden layer node of deep belief network to reduce the interference and redundancy. Secondly, the first predicting time node is determined by multi-kernel support vector data description to locate the stable degradation initiation node. Then, the temporal convolutional network is applied to predict the remaining useful life in the degradation stage, which improves the prediction accuracy. Finally, the effectiveness of the proposed method is verified by the accelerated fatigue experiment of a self-made robot.</p>","PeriodicalId":16235,"journal":{"name":"Journal of Mechanical Science and Technology","volume":"54 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remaining useful life prediction of industrial robot RV reducer with multiple deep networks and multicore support vector data description\",\"authors\":\"Guoai Ren, Zhihai Wang, Xiaoqin Liu, Feng Song\",\"doi\":\"10.1007/s12206-024-0703-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The remaining useful life prediction of Industrial robot RV reducer is challenging due to the strong redundancy, unstable degradation initiation point, and environmental interference that may obscure the key state information during long-term operation. To address this problem, this paper proposes a novel remaining useful life prediction method for robot RV reducer with multi-depth network and multi-kernel support vector data description. Firstly, the degradation features are constructed by the hidden layer node of deep belief network to reduce the interference and redundancy. Secondly, the first predicting time node is determined by multi-kernel support vector data description to locate the stable degradation initiation node. Then, the temporal convolutional network is applied to predict the remaining useful life in the degradation stage, which improves the prediction accuracy. Finally, the effectiveness of the proposed method is verified by the accelerated fatigue experiment of a self-made robot.</p>\",\"PeriodicalId\":16235,\"journal\":{\"name\":\"Journal of Mechanical Science and Technology\",\"volume\":\"54 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mechanical Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s12206-024-0703-y\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanical Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s12206-024-0703-y","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Remaining useful life prediction of industrial robot RV reducer with multiple deep networks and multicore support vector data description
The remaining useful life prediction of Industrial robot RV reducer is challenging due to the strong redundancy, unstable degradation initiation point, and environmental interference that may obscure the key state information during long-term operation. To address this problem, this paper proposes a novel remaining useful life prediction method for robot RV reducer with multi-depth network and multi-kernel support vector data description. Firstly, the degradation features are constructed by the hidden layer node of deep belief network to reduce the interference and redundancy. Secondly, the first predicting time node is determined by multi-kernel support vector data description to locate the stable degradation initiation node. Then, the temporal convolutional network is applied to predict the remaining useful life in the degradation stage, which improves the prediction accuracy. Finally, the effectiveness of the proposed method is verified by the accelerated fatigue experiment of a self-made robot.
期刊介绍:
The aim of the Journal of Mechanical Science and Technology is to provide an international forum for the publication and dissemination of original work that contributes to the understanding of the main and related disciplines of mechanical engineering, either empirical or theoretical. The Journal covers the whole spectrum of mechanical engineering, which includes, but is not limited to, Materials and Design Engineering, Production Engineering and Fusion Technology, Dynamics, Vibration and Control, Thermal Engineering and Fluids Engineering.
Manuscripts may fall into several categories including full articles, solicited reviews or commentary, and unsolicited reviews or commentary related to the core of mechanical engineering.