Prioritization of responsive maintenance tasks via machine learning-based inference

Abstract

Maintenance task prioritization is essential for allocating resources. It is estimated that almost 1/3 of the maintenance cost is wasted to unnecessary activities. Task prioritization is based on risk assessment that takes into account the probability of failure and the criticality of an asset. The criticality analysis is defined by the asset owner based on several parameters, among them safety, downtime cost, productivity, whilst the probability of failure is determined based on deterioration models, regular manual inspections, or installed sensors. Currently, the latter is an extremely complicated and labour intensive procedure, when multiple and different types of assets need to be managed. This paper proposes an innovative method that exploits the advances in mobile communications, social networking, Internet of Things and machine learning to address this shortcoming. This approach brings building elements and assets online using asset tags with an online ‘asset profile’ linked to it. Users of assets are able to scan these tags using a mobile phone app to not only see the information about those assets, but also enter ‘comments’ describing issues and problems on the profiles. These comments are processed through machine learning-based inference methods to estimate the probability that a failure has occurred. This paper validates the proposed method using historical data collected from the Estate Management, of the University of CambridgeInnovate U