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

ABSTRACT: 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.
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.
Lab-scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning
http://arxiv.org/abs/2212.14732v1