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Barry Taylor
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Proceedings Papers
Proc. ASME. GT1999, Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award; General, V004T04A008, June 7–10, 1999
Paper No: 99-GT-139
Abstract
The OEM, using a set of planned maintenance schedules that are based upon running hours or inspections, has traditionally formulated gas turbine maintenance practice. This maintenance concept has been reasonably successful, but it fails to take into account the introduction of new technologies than can provide the operator with a more meaningful insight into the condition of the turbine, perhaps even in a prognostic manner. The application of computer based technology to gas turbine condition monitoring can provide a significant reduction in maintenance costs. At the same time, condition-monitoring technology can provide an improvement in reliability and availability by extending the maintenance intervals and reducing the number of unplanned outages. This paper will discuss the introduction of an integrated approach to gas turbine control and engine condition assessment. This integrated approach enables the control to turn engine data into useful information and knowledge that assists the operator with trouble shooting and maintenance condition assessments thus lowering the overall engine maintenance costs.
Proceedings Papers
Proc. ASME. GT1999, Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award; General, V004T04A014, June 7–10, 1999
Paper No: 99-GT-307
Abstract
Magnetic chip detectors, vibration monitoring devices, and spectrographic oil analysis typically do not detect bearing distress until the bearing is in the latter stages of failure. Now, through the innovation of digital signal processing technology and a breakthrough in inductive sensors, it is possible to provide several months of advance notice on a bearing failure. Unlike magnetic chip detectors, this technology has the capability to track the shedding of both magnetic and nonmagnetic debris from the bearing. It is a true prognostic sensor that detects the first indications of a bearing spall and continues to track, in real-time, the quantities of wear debris being generated by the bearing. The data collected from the sensor can be changed into information such that with a particle trend graph it can clearly be seen when the bearing should be taken out of service prior to a turbine failure and the possibility of expensive secondary damage.