New Machine Learning algorithms and 4.0 HW solutions predict the future possibility of a failure
Predictive maintenance is the latest trend in the evolution of service policies: Smitec enables its customers to incorporate this new business intelligence model with a full package of state-of-the-art SW and HW solutions.
A proactive approach to the maintenance of industrial equipment
Predictive maintenance is a method of preventing asset failure by analyzing production data to identify patterns and predict issues before they happen.
Thanks to 4.0 technologies you can evaluate the performance of an asset by monitoring specific parameters and risk indicators. Based on real-time analysis of collected data, Machine Learning algorithms assess the optimal operating conditions of each machine.
On the one hand this strategy allows to predict probability and impact of equipment failure with a high degree of confidence and consistency, so as to schedule timely maintenance interventions. On the other, it prevents wasting resources on scheduling interventions earlier than needed.
Predictive algorithms and a new set of 4.0 HW solutions
Smitec contribution to this model of business intelligence consists in the development of predictive maintenance algorithms to optimise the performance of assets controlled by the Motornet System.
MNS is the open and flexible system designed and developed by Smitec for the automation of advanced tasks. Right now the new predictive algorithms are being tested on the equipment of Smitec key client SMI, awaiting for the official release.
Aware of the huge potential of predictive maintenance in Industry 4.0, Smitec engineers are developing a set of HW solutions to go with the new machine learning algorithms. Stay tuned, because in the near future the full package may be available to be incorporated also on equipment which feature third-parties automation solutions.
Predictive maintenance doesn't replace preventive maintenance, yet optimises it: scheduling timely pre-failure interventions cuts down on times and costs of single interventions and prevents unnecessary spare parts replacements.
Predictive maintenance aims at predicting the future possibility of a failure: thanks to the ML algorithms developed by Smitec, machines are able to self-diagnose potential failures, in order to schedule ad-hoc interventions.
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