Seeded Fault Testing For Conditioned Based Maintenance
DTB is fully capable in the equipment health management area. At the heart of any health management/condition based maintenance system is the prognostics framework that generates outputs alerting detection of an existing fault or the onset of a fault.
By tying the fault to the types of failures that can occur in the particular piece of equipment through testing to identify a monitored parameter (ie: vibration sensing), and relating the parameter to failure mode identification through algorithm development the system can predict certain things.
It identifies the criticality of the fault to the operation of the equipment, provides a timeframe when the fault is predicted to occur, and a confidence level of the assessment. It isolates the fault to the lowest level possible based on the information received and the algorithms developed. It then outputs quantitative symptoms, or evidence data indicating the fault, plotted over time on a graph.
DTB can design health based systems for a variety of applications and perform “Proof of Concept” programs that will demonstrate the heart of the health concept. It is based on using a particular piece of equipment ( eg: rotary shipboard, utility, rail car, wind, wastewater treatment) and performing what is termed “Seeded Fault Testing”.
A seeded fault test is a test in which a known fault component is operated in a condition similar to field conditions and then performance measurements are acquired through a data acquisition system and fed into the health software for engineering analysis.
The initial baseline tests are performed to establish the vibration profile of an “un faulted” system. Following the baseline test, DTB disassembles the rotary equipment and introduces the seeded faults identified in the field. Each seeded fault will be introduced independently and run on the test bench to characterize the vibration profile.
DTB analyzes all of the data recorded during the baseline and seeded fault testing and develops the algorithms required to be used with the health monitoring diagnostics system and are capable of discerning and identifying the seeded faults which relate to field failures.