Synthetic maintenance data generation for industrial assets based on historic statistical distribution using pseudo-random algorithm

Purpose The article aims to address the challenge of partial or complete absence of maintenance data records for industrial assets by generating synthetic maintenance data under a high-quality maintenance data structure established in the framework of International Organization for Standardization (ISO) 14224:2016. The preceding contributes to maintenance engineering, a strategy to obtain meaningful synthetic data in maintenance management analysis without exposing industrial assets to failures that may lead to undesired consequences. Design/methodology/approach The research was conducted under an experimental study aimed at generating synthetic maintenance data from historical statistical distributions of industrial assets. For experimental purposes, based on the criticality of the studied process context, the research was carried out on a centrifugal pump, with its primary data source from the Offshore Reliability Data Handbook (OREDA), from which the four failure modes with the highest failure rate and the non-maintainable components related to the failure rate by probability were selected. The data were processed using Python 3.10.12, using a methodology of standardizing the data structure, for which a pseudo-code was established. Findings The article addresses the generation of synthetic maintenance data using historical statistical distributions from the OREDA. Two sets of synthetic data were obtained for a centrifugal pump, with the second set maintaining originality by defining the maximum failure rate as the mean of the global failure rate based on accurate data, demonstrated with an error of 1.96%. This approach allows for objective decision-making when forecasting different scenarios, as the synthetic data set acquires its dynamics dependent on the statistical distribution of the failure rate by failure modes, evidenced by the error in the standard deviation. Originality/value The article focuses on generating synthetic maintenance data by developing an algorithm based on internationally recognized statistical distributions aligned with the international standards of ISO 14224:2016. This approach aims to create a synthetic maintenance dataset with maintenance records from which maintenance variables and indicators can be derived. These derived insights enable maintenance optimization through data-driven decision-making feedback loops.