Predictive maintenance is a technique that tries to predict imminent problems, forecast future failures and discovers criticalities when a piece of equipment might stop working so that proactive strategies can be applied just before that happens. These predictions can be done on the basis of equipment's condition which is estimated based on data collected with the help of condition monitoring sensors and strategies. To this aim, the predictive analytics has been measured to predict the belt tensioning level and to further support robot cycle labelling. A machine learning algorithm has been applied to smart data so as to forecast a tensioning level as per a new cycle of data.