Spatial Sampling with Fisher Information for Optimal Maintenance Management and Quality Assurance

Abstract

Maintenance management has been relying heavily on collecting asset condition information to plan for maintenance activities and budget allocation. Data collection is often conducted on a sampling basis because of resource constraints. There is thus a perceived need for the development of an effective sampling framework that can determine statistically representative samples, reflect the true level of maintenance (LOM) at state/region/station levels, and accommodate agencies’ requirements. The paper advances existing knowledge by presenting a systemic approach for a sampling scheme development to assist maintenance activity planning. The proposed method addresses how much and where the agencies need to collect asset condition data for accurate LOM estimation. The method integrates Fisher information with a spatial sampling technique that can be customized based on local agencies’ requirements, such as station balanced, spatially balanced, or others. The framework is showcased via an example application of the Signage Repair and Replace database maintained by the Utah Department of Transportation (UDOT). Four sampling methods that might be tempered to various needs are implemented. Sampling results are presented and compared against historical full asset inventory via similarity analysis. The proposed framework lays a strong theoretical foundation for maintenance asset sampling and is effective for estimating LOM at state, region, and station levels to assist with budget allocation. The method can be easily transferable and adaptable to other agencies for optimal maintenance management.