Original Date: 11/03/1996
Revision Date: 01/18/2007
Best Practice : Valve Monitoring
In 1985, Oak Ridge National Laboratory (ORNL) began a comprehensive aging assessment of motor operated valves (MOVs) at the request of the Nuclear Regulatory Commission who was concerned about the aging infrastructure of nuclear power plants across the country. ORNL focused on identifying ways to monitor the service wear of MOVs so that maintenance could be performed prior to failure. In addition, the study investigated methods for collecting data from a motor’s running current which could be acquired remotely and nonintrusively.
The ORNL study yielded two major diagnostic techniques: motor current signature analysis and electromagnetic valve monitoring. Research on MOV motor current led to the development of several signal conditioning and signature analysis methods, known as motor current signature analysis, which exploit the intrinsic ability of an electric motor to act as a transducer. Motor current signature analysis detects small, time-dependent load and speed variations generated within a MOV. The variations are then converted into revealing signatures which can identify degradation and incipient failures. This technique has led Oak Ridge’s capabilities in electrical signature analysis (ESA) which is currently being expanded into other applications.
Electromagnetic valve monitoring can detect the operating position of check valves through nonintrusive means. By placing electrical transmitter and receiver coils on the inlet and outlet side of a check valve, this technique can determine if the value is open, closed, or in a fluttering condition. Remote signal analysis can then verify the operating position of the valve. This technique is extremely useful in situations where a failed check valve could create explosive fuel or hazardous chemical spills.
On-line monitoring of MOVs, fans, and other rotating equipment has proven to be a cost-effective maintenance procedure for industrial and governmental operations. In addition, these systems could possibly provide data for decision-making processes on equipment replacement and operating costs.
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