A Risk Indicator in Asset Management to Optimize Maintenance Periods

Abstract

Different methodologies are nowadays employed to identify failure events in industrial process, allowing the decision makers to choose appropriate technical and organizational safety measures. The treatment of data in order to prevent dangerous events may affect significantly the diverse analyses and is reflected in the results. Quantification risk analysis is therefore one of the most critical areas in asset management (AM) as stated in the ISO 55000. In the same way, intelligent risk management should be one critical challenge of the Industry 4.0, since nowadays and by using new technologies, it is possible to gather large amounts of data extrapolated from the physical assets. With all the above, this paper is intended to understand uncertainty, trying to reduce the risk of dangerous events by the treatment of big data. Particularly, a time window is obtained showing minimum and maximum thresholds for the best time to apply a preventive maintenance task, together with other interesting statistics.