Skip to main navigation Skip to main content
  • E-Submission

JKSPE : Journal of the Korean Society for Precision Engineering

OPEN ACCESS
ABOUT
BROWSE ARTICLES
EDITORIAL POLICIES
FOR CONTRIBUTORS
Article

고차 시간-주파수 해석과 신경망 회로를 이용한 냉장고 압축기의 건전성 연구

신태진, 이상권, 장지욱

A Study on Health Monitoring of a Refrigerator Compressor Based on Higher Order Time-Frequency Analysis and Artificial Neural Network

Tae-Jin Shin, Sang-Kwon Lee, Ji Uk Jang
JKSPE 2012;29(12):1313-1320.
Published online: December 1, 2012
  • 6 Views
  • 0 Download
  • 0 Crossref
  • 0 Scopus
prev next

Condition monitoring of the reciprocating compressor is important task. As a traditional method, health monitoring system of refrigerator depends on decision of a skilled person based on his experience. However, the skilled person cannot monitor all the compressors completely. If a sampled compressor is faulty, thousands of compressors manufactured at that place are regarded as faulty compressors. Therefore it is necessary to monitor all compressors in the production line. In the paper real time health monitoring system is developed based on high order time frequency method and artificial neural network. The system is installed in the mass production line. The result of the application has been very successful, and currently the system is working very well on the production line.

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

Include:

A Study on Health Monitoring of a Refrigerator Compressor Based on Higher Order Time-Frequency Analysis and Artificial Neural Network
J. Korean Soc. Precis. Eng.. 2012;29(12):1313-1320.   Published online December 1, 2012
Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:
Include:
A Study on Health Monitoring of a Refrigerator Compressor Based on Higher Order Time-Frequency Analysis and Artificial Neural Network
J. Korean Soc. Precis. Eng.. 2012;29(12):1313-1320.   Published online December 1, 2012
Close