A post-prognostics decision framework for cell site using Cloud computing and Internet of Things

Maintenance is one of the main factors of production process. The aim of maintenance strategy is not just to repair and maintain equipment in a good condition, but to implement efficient maintenance solutions to ensure the good function while minimizing the cost of maintenance. Predictive maintenance strategy start by collecting data from sensors, analyze this data and predict the malfunction or failure in the system. As a result, with this information, we try to find the optimal solution for maintenance. Prognostics Health Manager (PHM) offers significant benefits for maintenance. It predicts the future behavior of a system as well as its remaining useful life. However when factories have a large number of asset with mobile and stationary equipment in different geographically sites. Making decision and collecting data become difficult to be done. In this study we interested in stationary equipment geographically distributed; and we propose a decision post-prognostics framework to help engineers to take the optimal decision for maintenance operation in order to minimize maintenance cost. In order to enhance the post-prognostics decision, we propose a framework based on Iot technology for real-time sensing to collect data from equipment and Cloud computing paradigm for resources management and information processing.