A Clustering-Based Framework for Performance Degradation Prediction of Slewing Bearing Using Multiple Physical Signals
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 5, Issue 2
Abstract
Diagnosing the failure or predicting the performance state of low-speed and heavy-load slewing bearings is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures focus on the performance prediction of small rolling bearings, while studies on slewing bearings' health evaluation are very rare. Among these rare studies, supervised or unsupervised data-driven models are often used alone, few researchers devote to remaining useful life (RUL) prediction using the joint application of two learning modes which could fully take diversity and complexity of slewing bearings' degradation and damage into consideration. Therefore, this paper proposes a clustering-based framework with aids of supervised models and multiple physical signals. Correlation analysis and principle component analysis (PCA)-based multiple sensitive features in time-domain are used to establish the performance recession indicators (PRIs) of torque, temperature, and vibration. Subsequently, these three indicators are divided into several parts representing different degradation periods via optimized self-organizing map (OSOM). Finally, corresponding data-driven life models of these degradation periods are generated. Experimental results indicate that multiple physical signals can effectively describe the degradation process. The proposed clustering-based framework is provided with a more accurate prediction of slewing bearings' RUL and well reflects the performance recession periods. This article is available in the ASME Digital Collection at https://doi.org/10.1115/1.4042843.
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Copyright © 2019 by ASME.
History
Received: Aug 22, 2018
Revision received: Jan 23, 2019
Published online: Apr 15, 2019
Published in print: Jun 1, 2019
Authors
Funding Information
National Natural Science Foundation of China10.13039/501100001809: 51875273
Six Talent Peaks Project in Jiangsu Province10.13039/501100010014: 2016-GDZB-033
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