Paper Info
Title
An Experimental Study On Condition Diagnosis For Thrust Bearings In Oscillating Water Column Type Wave Power Systems
Abstract
In order to utilize wave energy, various wave power systems are being actively researched and developed and interest in them is increasing. To maximize the operational efficiency, it is very important to monitor and maintain the fault of components of the system. In recent years, interest in the management cost, high reliability and facility utilization of such systems has increased. In this regard, fault diagnosis technology including fault factor analysis and fault reproduction is drawing attention as an important main technology. Therefore, in this study, to reproduce and monitor the faults of a wave power system, firstly, the failure mode of the system was analyzed using FMEA analysis. Secondly, according to the derived failure mode and effect, the thrust bearing was selected as a target for fault reproduction and a test equipment bench was constructed. Finally, with the vibration data obtained by conducting the tests, the vibration spectrum was analyzed to extract the features of the data for each operating status; the data was classified by applying the three machine learning algorithms: naive Bayes (NB), k-nearest neighbor (k-NN), and multi-layer perceptron (MLP). The criteria for determining the fault were derived. It is estimated that a more efficient fault diagnosis is possible by using the standard and fault monitoring method of this study.
Year
DOI
Venue
2021
10.3390/s21020457
SENSORS
Keywords
DocType
Volume
wave power system, oscillating water column type wave power system, thrust bearing, fault reproduction, fault diagnosis, FMEA, vibration spectrum, machine learning algorithm
Journal
21
Issue
ISSN
Citations 
2
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Tae Wook Kim18617.66
Jaewon Oh210111.06
Cheonhong Min301.01
Se Yun Hwang401.01
Min-Seok Kim516927.35
Jang Hyun Lee6123.42