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Performance Analysis on Early Detection of Fault Symptom of a Pump with Abnormal Signals
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 Title & Authors
Performance Analysis on Early Detection of Fault Symptom of a Pump with Abnormal Signals
Jung, Jae-Young; Lee, Byoung-Oh; Kim, Hyoung-Kyun; Kim, Dae-Woong;
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 Abstract
As a method to improve the equipment reliability, early warning researches that can be detected fault symptom of an equipment at an early stage are being performed out among developed countries. In this paper, when abnormal signal is input to actual normal signal of a pump, early detection studies on pump`s fault symptom were carried out with auto-associative kernel regression as an advanced pattern recognition algorithm. From analysis, correlations among power of motor driving pump, discharge flow of pump, power output of pump, and discharge pressure of pump are exited. When the abnormal signal is input to one of those normal signals, the other expected values are changed due to the influence of the abnormal signal. Therefore, the fault symptom of pump through the early-warning index is able to detect at an early stage.
 Keywords
Equipment Reliability;Early Warning;Fault Symptom;Pattern Recognition;Auto-Associative Kernel Regression(AAKR);Correlation;Early-warning Index;
 Language
Korean
 Cited by
 References
1.
B. O. Lee, 2014, "Trend Analysis on the Cylinder Pressure Signal of Pielstick-type Diesel Engine", Autumn Conference Proceeding of the Korean Society for Power System Engineering, pp. 161-162.

2.
J. H. Park, K. H. Choi and S. G. Lee, 2009, "Analysis and Evaluation Study on Diesel Generator Engine Operation Signature", Journal of the Korean Society for Power System Engineering, Vol. 13, No. 5, pp. 82-88.

3.
C. G. Atkeson, A. W. Moore and S. Schaal, 1997, "Locally Weighted Learning", Artificial Intelligence Review, Vol. 11, pp. 11-73. crossref(new window)

4.
B. P. Rasmussen, 2003, "Prediction Interval Estimation Techniques for Empirical Modeling Strategies and their Applications to Signal Validation Tasks", Doctoral Thesis of University of Tennessee, pp. 49-311.

5.
G. Kauermann, M. Marlene and J. C. Raymond, 1998, "The efficiency of bias-corrected estimators for nonparametric kernel estimation based on local estimating equations," Statistics and Probability Letters, Vol. 37, pp. 41-47. crossref(new window)

6.
Wand and Jones, 1997, "Kernel Smoothing, Monographs on Statistics and Applied Probability", Chapman & Hall.

7.
J. W. Hines, D. R. Garvey, R. Seibert and A. Usynin, 2008, "Technical Review of On-Line Monitoring Techniques for Performance Assessment Volume 2 : Theoretical Issues", NUREG/CR-6895 Vol. 2, pp. 23-51.

8.
S. H. An, 2010, "A Study on Online Monitoring System Development using Empirical Models", Doctoral Thesis of Korea Advanced Institute of Science and Technology, pp. 29-39.

9.
J. W. Hines and D. R. Garvey, 2006, "Development and Application of Fault Detectability Performance Metrics for Instrument Calibration Verification and Anomaly Detection", Journal of Pattern Recognition Research 12-15, pp. 2-7.

10.
K. M. Ramachandran and C. P. Tsokos, 2009, "Mathematical Statistics with Applications", Academic Press, pp. 174-197.