estimation of probability density functions of damage parameter for valve leakage detection in reciprocating pump used in nuclear power plants
Clicks: 208
ID: 167394
2016
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
30.0
/100
205 views
8 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
This paper presents an advanced estimation method for obtaining the probability density functions of a damage parameter for valve leakage detection in a reciprocating pump. The estimation method is based on a comparison of model data which are simulated by using a mathematical model, and experimental data which are measured on the inside and outside of the reciprocating pump in operation. The mathematical model, which is simplified and extended on the basis of previous models, describes not only the normal state of the pump, but also its abnormal state caused by valve leakage. The pressure in the cylinder is expressed as a function of the crankshaft angle, and an additional volume flow rate due to the valve leakage is quantified by a damage parameter in the mathematical model. The change in the cylinder pressure profiles due to the suction valve leakage is noticeable in the compression and expansion modes of the pump. The damage parameter value over 300 cycles is calculated in two ways, considering advance or delay in the opening and closing angles of the discharge valves. The probability density functions of the damage parameter are compared for diagnosis and prognosis on the basis of the probabilistic features of valve leakage.
Abstract Quality Issue:
This abstract appears to be incomplete or contains metadata (201 words).
Try re-searching for a better abstract.
| Reference Key |
lee2016nuclearestimation
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Jong Kyeom Lee;Tae Yun Kim;Hyun Su Kim;Jang-Bom Chai;Jin Woo Lee |
| Journal | Journal of hazardous materials |
| Year | 2016 |
| DOI |
10.1016/j.net.2016.04.007
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.