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http://dx.doi.org/10.14775/ksmpe.2016.15.4.023

A Study on Fault Diagnosis Algorithm for Rotary Machine using Data Mining Method and Empirical Mode Decomposition  

Yun, Sang-hwan (Consortium organization, Changwon UNIV.)
Park, Byeong-hui (School of Mechanical Engineering, Changwon UNIV.)
Lee, Changwoo (School of Mechanical Engineering, Changwon UNIV.)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.15, no.4, 2016 , pp. 23-29 More about this Journal
Abstract
Rotary machine is major equipment in industry. The rotary machine is applied for a machine tool, ship, vehicle, power plant, and so on. But a spindle fault increase product's expense and decrease quality of a workpiece in machine tool. A turbine in power plant is directly connected to human safety. National crisis could be happened by stopping of rotary machine in nuclear plant. Therefore, it is very important to know rotary machine condition in industry field. This study mentioned fault diagnosis algorithm with statistical parameter and empirical mode decomposition. Vibration locations can be found by analyze kurtosis of data from triaxial axis. Support vector of data determine threshold using hyperplane with fault location. Empirical mode decomposition is used to find fault caused by intrinsic mode. This paper suggested algorithm to find direction and causes from generated fault.
Keywords
Support Vector Machine; Empirical Mode Decomposition; Kurtosis; Skewness; Fault Diagnosis;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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