Predictive maintenance plant assessment
- Maintenance & Diagnostics, LLC, Eddystone, PA (United States)
Many electric power utilities are currently employing machine condition monitoring technologies as a means of reducing forced outages and controlling repair costs. These condition monitoring technologies are a set of {open_quotes}tools{close_quotes} to help maintenance and operations personnel identify and repair machinery problems before catastrophic failure occurs. In this way, the analysis of this data becomes a vital link in the daily operation of the plant. Machinery vibration analysis, machinery balancing and alignment, motor current testing, periodic thermography surveys, analysis of lubrication oil samples, performance monitoring, chemistry monitoring, ultrasonic leak detection surveys, and non-destructive inspections are some of the technologies available to perform this condition monitoring. However, even for a small or medium size plant, the sheer volume of data provided by applying all or some of these technologies can be staggering, not to mention the need to consider equipment process parameters such as pressure, temperature, flow, amps, etc. Therefore, these data tools need to be integrated into a single, coordinated condition monitoring program. This integrated analysis, along with the decision-making and subsequent corrective operational or maintenance action, is called the {open_quotes}Predictive Maintenance (PDM) Program.{close_quotes} The goal of Predictive Maintenance is to reduce operation and maintenance costs and increase equipment reliability by extending the effective operating life of the equipment under surveillance, until such a time when repairs can be conveniently made. This paper describes the process that is used to identify existing resources and to specify new technologies necessary to implement an integrated Predictive Maintenance Program in a plant. The technical, organizational and financial issues surrounding predictive maintenance are examined, as well as the decision processes involved.
- Research Organization:
- Electric Power Research Inst., Palo Alto, CA (United States); Baltimore Gas and Electric Co., MD (United States)
- OSTI ID:
- 401922
- Report Number(s):
- EPRI-TR--106753; CONF-960719--
- Country of Publication:
- United States
- Language:
- English
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