Remaining useful life prediction of ball-bearings based on high-frequency vibration features

Author:

Behzad Mehdi1ORCID,Arghand Hesam Addin1,Rohani Bastami Abbas2

Affiliation:

1. School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

2. Department of Mechanical and Energy Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran

Abstract

Selecting appropriate features from the vibration condition monitoring data of ball-bearings is one of the main challenges in the application of data-driven methods for remaining useful life prediction purpose. In this article, a new feature based on the high-frequency vibration of ball-bearings is proposed. The feed forward neural network will be used for training and prediction. The experimental data of the bearing accelerated life in the PROGNOSTIA test (published in PHM 2012 IEEE conference) are used to verify the method. The results obtained by applying new features are compared with those of two popular features in the time domain (RMS and kurtosis) for prognostic purpose. Applying the proposed feature shows more accurate estimation of the bearings’ remaining useful life.

Publisher

SAGE Publications

Subject

Mechanical Engineering

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