首页|Guangdong University of Technology Researcher Updates Current Study Findings on Robotics (Semi-Supervised Informer for the Compound Fault Diagnosis of Industria l Robots)
Guangdong University of Technology Researcher Updates Current Study Findings on Robotics (Semi-Supervised Informer for the Compound Fault Diagnosis of Industria l Robots)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on robotics is the subjec t of a new report. According to news reporting originating from Guangzhou, Peopl e's Republic of China, by NewsRx correspondents, research stated, "The increasin g deployment of industrial robots in manufacturing requires accurate fault diagn osis." The news journalists obtained a quote from the research from Guangdong Universit y of Technology: "Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent di agnosis methods heavily rely on supervised learning with abundant labeled data." According to the news reporters, the research concluded: "To address this issue, this paper presents a semi-supervised Informer algorithm for fault diagnosis mo deling, leveraging the Informer model's longand short-term memory capabilities and the benefits of semi-supervised learning to handle the diagnosis of a small amount of labeled data alongside a substantial amount of unlabeled data. An exp erimental study is conducted using real-world industrial robot monitoring data t o assess the proposed algorithm's effectiveness, demonstrating its ability to de liver accurate fault diagnosis despite limited labeled samples."
Guangdong University of TechnologyGuan gzhouPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsSupervised Learning
NETL
NSTL
