论文标题
实验室规模的振动分析数据集和用于机械故障诊断机器学习的基线方法
Lab-scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning
论文作者
论文摘要
对工厂中机器状况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况的良好指示。本文介绍了来自实验室计算机的振动信号数据集。数据集包含四种不同类型的机器条件：正常，不平衡，未对准和轴承故障。三种机器学习方法（SVM，KNN和GNB）评估了数据集，并通过1倍测试中的一种方法获得了完美的结果。由于数据是平衡的，因此使用加权精度（WA）评估算法的性能。结果表明，表现最佳的算法是5倍的交叉验证的SVM，WA为99.75 \％。该数据集以CSV文件的形式提供，以https://zenodo.org/record/7006575的开放和免费存储库提供。
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.
