Lab-Scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning

Abstract

Motivation: 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. Purpose: 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 onefold test. Results: The performance of the algorithms is evaluated using weighted accuracy (WA), since the data are balanced. The results show that the best-performing algorithm is the SVM with a WA of 99.75% on the fivefold cross-validations. The dataset is provided in the form of CSV files in an open and free repository at https://zenodo.org/record/7006575.