Application of statistical techniques in assisting maintenance activities at the Neal South Fossil Power Plant

The objective of the research was to assist the maintenance activities at the Neal South Fossil Power Plant. The plant engineers had the task of maintaining mammoth number of equipments that tended to fail at random. This created enormous problems in keeping the plant up and running. The sheer number of equipments and number of times they failed for variety of reasons under different circumstances gives rise to a complex situation where engineers get buried in a deluge of data. This in turn means that they are unable to pinpoint where their major problems lie and fail to tackle it effectively with the limited resources they have. To tackle this problem as a first step we try to isolate the most problematic pieces of equipment. This was done using the pareto principle which helps to isolate the "vital few" from the "trivial many." The isolation of the vital few was mainly done based on outage and worktype. Having isolated the vital few it was felt that we should go further and isolate the most problematic piece of equipment. It was felt that any maintenance strategy applied as a part of further research could be applied to this piece of equipment to test its viability. Multiattribute decision analysis was used in isolating the most problematic piece of equipment. The second step in this research tried to tackle the problem of making the maintenance activity at the plant more effective. The plant had an existing R.C.M. program which listed the failure modes for each and every equipment and the best course of action for that eventuality. However the R.C.M. could not track the failure mode and therefore was only a reactive procedure. To tackle this problem we listed measurable characteristics corresponding to the modes of failure and tracked them using a S.P.C. chart. This was a more pro-active and effective strategy. This research was demonstrated successfully using real data from the plant.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Industrial Engineering

Year dc:date.issued

1997

Author dc:creator

• Vaidyanathan, Anand

Advisor dc:contributor.advisor

• Heising, Carolyn

Language dc:language.iso

en

DOI

https://doi.org/10.31274/td-20250603-38

OAI identifier oai:identifier

oai:dr.lib.iastate.edu:20.500.12876/ywAbM4lv