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

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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.

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Acknowledgements

The authors would like to thank enDAQ for providing calibrated vibration sensor, LOG-0002-100G-DC-8GB-PC Shock and Vibration Sensor, and data acquisition system (enDAQ LAB) used in this research. Parts of this research were supported by the New Energy and Industrial Technology Development Organization (NEDO), Japan, under Project No. JPNP20006.

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Authors and Affiliations

  1. Department of Engineering Physics, Sepuluh Nopember Institute of Technology, ITS Sukolilo Campus, Surabaya, 60111, Jawa Timur, Indonesia

    Bagus Tris Atmaja, Haris Ihsannur,  Suyanto & Dhany Arifianto

  2. National Institute of Advanced Industrial Science and Technology, Tsukuba, 3058560, Japan

    Bagus Tris Atmaja

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  1. Bagus Tris Atmaja
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  2. Haris Ihsannur
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  3. Suyanto
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Correspondence to Bagus Tris Atmaja.

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Atmaja, B.T., Ihsannur, H., Suyanto et al. Lab-Scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning. J. Vib. Eng. Technol. 12, 1991–2001 (2024). https://doi.org/10.1007/s42417-023-00959-9

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  • DOI: https://doi.org/10.1007/s42417-023-00959-9

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