MATEC Web Conf.
Volume 211, 2018The 14th International Conference on Vibration Engineering and Technology of Machinery (VETOMAC XIV)
|Number of page(s)
|TP4: Machinery and structural dynamics
|10 October 2018
Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines
Dynamics Laboratory, School of Mechanical, Aerospace and Civil Engineering (MACE), The University of Manchester,
* Corresponding author: firstname.lastname@example.org
The development of technologies for the maintenance industry has taken an important role to meet the demanding challenges. One of the important challenges is to predict the defects, if any, in machines as early as possible to manage the machines downtime. The vibration-based condition monitoring (VCM) is well-known for this purpose but requires the human experience and expertise. The machine learning models using the intelligent systems and pattern recognition seem to be the future avenue for machine fault detection without the human expertise. Several such studies are published in the literature. This paper is also on the machine learning model for the different machine faults classification and detection. Here the time domain and frequency domain features derived from the measured machine vibration data are used separated in the development of the machine learning models using the artificial neutral network method. The effectiveness of both the time and frequency domain features based models are compared when they are applied to an experimental rig. The paper presents the proposed machine learning models and their performance in terms of the observations and results.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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