摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器人的新研究是一份新报告的主旨。根据NewsRx记者从中华人民共和国广州发回的新闻报道，研究表明：“工业机器人在制造业中的部署越来越多，需要准确的故障诊断。”新闻记者从广东工业大学的研究中得到一句话:“在线监测数据通常由大量未标记数据和少量标记数据组成。传统的智能诊断方法严重依赖于有监督学习和大量标记数据。”据新闻记者介绍，研究结论是：“针对这一问题，本文提出了一种半监督的故障诊断模型算法，”利用Informer模型的长短期记忆能力和半监督学习的优点，处理少量标记数据和大量未标记数据的诊断。利用真实工业机器人监测数据进行了实验研究，以评估所提算法的有效性，证明了该算法在标记样本有限的情况下仍能进行肝脏精确故障诊断。
Abstract
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."
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