Original Date: 03/08/1999
Revision Date: 01/18/2007
Information : Pareto Analysis Strategy
The Pareto principle states that 20% of the causes usually account for 80% of the effects. This distribution is typically the case in process and product improvements. Observations often show that the majority of problems stem from relatively few causes. At the Applied Research Laboratory at the Pennsylvania State University (ARL Penn State), attention is being focused on maintenance activities with the greatest effect on asset performance, availability, and safety, while diverting energy away from those activities with little or no effect. A careful observation of the Pareto effect provides valuable insight in the development of an effective strategy for maintenance activities in a given application. Pareto analysis that uses Pareto diagrams, cause and effect methods, and histograms can intensify critical areas for which concentration of improvement efforts yield the most valuable returns.
Creating a Pareto chart as an aid to designing or improving a maintenance strategy begins by identifying different categories of failure causes and compiling various failure effects, such as cost and downtime contributed by each category. These categories are then arranged in a bar graph in descending order from largest to smallest contributor. A cumulative failure curve is superimposed onto the graph. By locating the area where the curve begins to level out (knee), users can identify the contributors of most problems. If a knee region is not readily determined, the user should identify the group of categories which make up at least 60% of the failures. The ARL Penn State compares the indications of this chart to the distribution of effort in the current maintenance plan.
The ARL Penn State is currently working with a consortium of companies to demonstrate the applicability of integrated prognostic health management technology for designing a new generation, turbofan fighter engine. A Pareto analysis was employed to help identify the appropriate application of wireless-distributed sensor, vibration- based health monitoring for the engine. The scope of the task was defined to include the engine main bearing and accessory gear train systems. Information on the failure modes for these systems was primarily gathered from engine manufacturer field service representative reports, as well as meetings and conversations with engine support and design engineers. Safety and asset availability were priority considerations; therefore, failures that led to in-flight emergency and abort events were compiled into one Pareto chart. Failures leading to engine removal were compiled into a separate chart. Another chart was created to show the distribution of all types of failures over the different failure modes in order to reveal those contributors deserving priority attention due to the number of failures.
Although the ARL Penn State’s chart did not match the typical 20%-80% Pareto distribution, the analysis did suggest categories which merited prioritized attention, and the most effective application of vibration-based health monitoring could be assessed. The analysis further suggests that a large majority of maintenance effort could have been avoided with an accurate, automated isolation of observed, fault condition sources. A change in maintenance procedures would be the most appropriate solution based on this analysis.
The ARL Penn State’s study exhibits a strong understanding of the value of Pareto analysis toward effective maintenance strategies. The benefits to the manufacturing industry to use Pareto analyses in designing maintenance and other quality improvement strategies can be realized by focusing efforts on those activities which yield the most beneficial results.
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