摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx Jour Nalists在土耳其杜兹的新闻报道，研究表明：“预测维修(PdM)是一种利用传感器、数据分析和机器学习算法等先进技术，有效地管理制造业机械设备的维修计划，预测潜在故障，并对PdM数据中各种故障自动分类方法进行了研究。”新闻记者从杜兹大学的研究中获得了一句话：“我们首先介绍了用浅层机器学习(SML)方法进行故障分类的性能评估，这些方法包括决策树、支持向量机、k近邻和一维深度学习(DL)技术，如1D-LeNet、1D-AlexNet和1D-VGG16.”然后，我们应用归一化，这是一种缩放技术，在数据集中移动和重新缩放特征。将分类算法重新应用到归一化的数据集上，并给出了性能表，并与已有的结果进行了比较。此外，与现有的研究相比，我们通过随机选择原始数据集中所有故障类型的正常数据和所有故障数据来生成平衡数据集组。数据集组由100个不同的代表组成，记录每个代表的性能分数并呈现最大的s核心。研究中使用的所有方法同样适用于这些组。 "
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting from Duzce, Turkey, by NewsRx jour nalists, research stated, "Predictive maintenance (PdM) is implemented to effici ently manage maintenance schedules of machinery and equipment in manufacturing b y predicting potential faults with advanced technologies such as sensors, data a nalysis, and machine learning algorithms. This paper introduces a study of diffe rent methodologies for automatically classifying the failures in PdM data." The news journalists obtained a quote from the research from Duzce University: " We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector M achines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. W e reapply classification algorithms to the normalized dataset and present the pe rformance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset gro ups by randomly selecting normal data and all faulty data for all fault types fr om the original dataset. The dataset groups are generated with 100 different rep etitions, recording performance scores for each one and presenting the maximum s cores. All methods utilized in the study are similarly employed on these groups. "
NETL
NSTL