Data preparation and preprocessing for broadcast systems monitoring in PHM framework

Nowadays, companies producing goods use production systems that are equipped by different sensors in order to monitor efficiently their behavior. Most of the time, the information collected by these sensors is mainly used for production monitoring rather than to analyzing the state of health of the production system. By so doing, these companies have a large and growing amount of data at their disposal. These data make it possible to extract information and knowledge for a better control of the system in order to improve its efficiency and reliability. With the emergence of Prognostics and Health Management (PHM) paradigm few years ago, it has become possible to study the state of health of an equipment and predict its future evolution. Globally, the principle of PHM is to transform a set of raw data gathered on the monitored equipment into one or more health indicators. In this framework, the present paper addresses issues related to raw data. A generic approach is proposed for obtaining monitoring data that are reliable and exploitable in a PHM application. The proposed approach is based on 2 steps: collecting data and preprocessing data. This approach will be applied to a real world case in broadcast industry to show its feasibility.