标题： 实验室规模振动分析数据集和基于机器学习的机械故障诊断基准方法 显示英文标题

标题： Lab-scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning

摘要： 在工厂中对机器状态的监测对于制造业的生产至关重要。 机器的突然故障会停止生产并导致收入损失。 机器的振动信号是其状态的良好指示器。 本文介绍了一个来自实验室规模机器的振动信号数据集。 该数据集包含四种不同的机器状态：正常、不平衡、不对中和轴承故障。 三种机器学习方法（SVM、KNN 和 GNB）评估了该数据集，在 1 次测试中，其中一种方法获得了完美的结果。 由于数据是平衡的，使用加权准确率（WA）来评估算法的性能。 结果表明，表现最好的算法是 SVM，在 5 次交叉验证中的 WA 为 99.75%。 该数据集以 CSV 文件的形式提供在一个开放且免费的存储库中，网址为 https://zenodo.org/record/7006575。 显示英文摘要

摘要： The monitoring of machine conditions in a plant is crucial for production in manufacturing. A sudden failure of a machine can stop production and cause a loss of revenue. The vibration signal of a machine is a good indicator of its condition. This paper presents a dataset of vibration signals from a lab-scale machine. The dataset contains four different types of machine conditions: normal, unbalance, misalignment, and bearing fault. Three machine learning methods (SVM, KNN, and GNB) evaluated the dataset, and a perfect result was obtained by one of the methods on a 1-fold test. The performance of the algorithms is evaluated using weighted accuracy (WA) since the data is balanced. The results show that the best-performing algorithm is the SVM with a WA of 99.75\% on the 5-fold cross-validations. The dataset is provided in the form of CSV files in an open and free repository at https://zenodo.org/record/7006575.