Repository logo
Log In(current)
  1. Home
  2. Colleges & Schools
  3. Graduate School
  4. Masters Theses
  5. Signature monitoring and automated diagnosis of motor-operated valves
Details

Signature monitoring and automated diagnosis of motor-operated valves

Date Issued
December 1, 1997
Author(s)
Snowden, Scott A.
Advisor(s)
Belle R. Upadhyaya
Additional Advisor(s)
J. W. Hines, L. F. Miller
Abstract

Nonintrusive diagnostic methods provide the identification of malfunctions in plant components during normal plant operations allowing the avoidance of catastrophic failures and the associated costs. The operability of a plant component driven by an electrical motor can be determined by analysis of measured electrical variables from the motor. The result of the current research is an expert system for the diagnosis of motor- operated valves (MOVs) in nuclear power plants through analysis of the motor power signature and an automated marking of the power signature for different valve types.


The PC-based fuzzy-expert system, PowerMOV, identifies events in the motor current, motor voltage, switch current and total real power (TRP) signatures from MOVS and uses these events to diagnose degradations identified by Nuclear Regulatory Commission (NRC) Generic Letter 89-10. The enhanced system, PowerMOV, was interfaced with Motor Power Monitor (MPM) which provides the data acquisition and power calculations. Field data used in this research were acquired from operating nuclear power stations. PowerMOV successfully detects events and identifies degradations for gate, globe and butterfly valves. A Microsoft Access 2.0 database was used to store all marked events and calculated input parameters for PowerMOV for all previous tests of each MOV including the baseline data. Data storage for a large number of MOVs was provided in the database. The information stored in the database include MOV operability parameters for use in the trending of MOV degradation.

Degree
Master of Science
Major
Nuclear Engineering
File(s)
Thumbnail Image
Name

Thesis97.S62.pdf_AWSAccessKeyId_AKIAYVUS7KB2IXSYB4XB_Signature_PtJeUzGLGpZxwcLr9St0SLV70fw_3D_Expires_1712841621

Size

4.66 MB

Format

Unknown

Checksum (MD5)

da3ea91c57eb583feb8227ca45ea3638

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Privacy policy
  • End User Agreement
  • Send Feedback
  • Contact
  • Libraries at University of Tennessee, Knoxville
Repository logo COAR Notify