Publication Date:
2018-04-06
Description:
Sensors, Vol. 18, Pages 1111: Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring Sensors doi: 10.3390/s18041111 Authors: Si-Yuan Wang Ding-Xin Yang Hai-Feng Hu As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants’ multi-parameters and the bearings’ wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes.
Electronic ISSN:
1424-8220
Topics:
Chemistry and Pharmacology
,
Electrical Engineering, Measurement and Control Technology
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