In the paradigm of Industry 4.0 digitization becomes fundamental for the management of production processes. One innovative concept is the Predictive maintenance, based on Internet of Things and Machine Learning that allows you to optimize maintenance works to reduce risk of machine downtime or unexpected breakdowns.
Predictive maintenance aims to detect and signal variations that may require technical work through information on the status of the plant during the production activity to "foresee" a possible malfunction.
It can easily be confused with Preventive maintenance, which instead consists of scheduled control and service activities on the automation system, however necessary to maintain the state of health of the plant.
A first form of Predictive Maintenance consisted in the manual collection of the necessary data, but today thanks to a fast and extensive development of the Internet of Things this information can be provided in real time and digitally archived to be studied and developed with AI and Machine Learning.
Predictive Maintenance leverages first cutting-edge technologies, above all monitoring sensors that collect data during plant activity and which provide digital information in real time useful for analyzing its state.
Fundamental for the implementation of Predictive Maintenance is the interconnection of machines with the company's digital infrastructures, such as cloud services and databases, as the information relating to the conditions of the machines collected by the sensors is transmitted to a digital archiving system, to then be shared with other resources.
This information is analyzed by predictive algorithms, specifically structured to understand from the data received if something is about to break or the level of wear of a machine. Artificial Intelligence, through these predictive algorithms, creates reports that identify trends in the production system and indicate where maintenance is required.
Sinteco has started the first tests for the implementation of Predictive Maintenance on custom automation systems, using innovative technologies for data collection and programming ad hoc algorithms for reporting useful for monitoring and predictive intervention.
