Facilities components’ reliability & maintenance services self-rating through big data processing

Abstract

Abstract The availability of big data in the information modelling of buildings can be useful to improve maintenance strategies and activities that are integrated in a digital twin. In some countries, such as Italy, tender specifications for public works must avoid any reference to specific brands and models, both in building design and maintenance services: quality levels and service-life objectives must be defined solely through performance specifications with reference to national or international standards. This could be a critical issue when considering reliability and serviceability of facility components, because there are no official methods about the ratings or measurements on the aforementioned performances. To help solving this concern, a method is proposed to broaden the scope of the big data collected from IoT applied to facility components, so as to feed a general and public database capable of normalizing data on faults and the effects of maintenance interventions, e.g. by correlating them with actual running times and operating conditions. In this way, each component on the market can theoretically feed a public and accessible database that collects reports on the occurrence of faults and the maintenance results, thus statistically processing its propensity for durability, the effectiveness of maintenance, the maintainability propensity of components as well as their reliability (e.g. by assessing the interval between maintenance interventions). In this way, a standardization of reliability, maintainability and durability performances ratings for components and serviceability performance rating for facility maintenance services could boost the facility quality design and improve the maintenance management.