Predictive maintenance is one of the main benefits deriving from Industry 4.0. This is an intelligent type of maintenance, capable of estimating the residual life time of machinery and components to improve the reliability of the entire system. This happens thanks to the continuous data analysis created by the machinery which is connected to a network. 

There are two main advantages that can be ensured by the correct implementation of a predictive maintenance strategy: predicting when failures may occur and being able to schedule timely interventions, thus reducing costly downtime and plant shutdowns as well as spare parts costs. 

In the long term, companies can count on an extension of the useful life cycle of their machines, also reducing the costs associated with spare parts in the warehouse. Therefore, companies that are able to consistently apply this type of strategy can count on evident savings, in terms of maintenance costs and a significant productivity gain. 

From an infrastructural point of view, setting up correct Predictive Maintenance is anything but simple and obvious, considering the heterogeneity of machinery and solutions present in real industrial contexts. Powua – the Avantune platform of IoT Orchestration, allows one to acquire data in real time from heterogeneous devices, interfacing PLC (Programmable Logic Controller), sensors and intelligent machinery, and can be connected to external data sources such as SCADA, ERP, MES and CMMS.

Alert

With the metering / monitoring module it is possible to process and correlate the information detected, to set critical thresholds that activate alerts and interventions, for each machine or component.

Sensors

Smart sensors allow one to carry out diagnostics, to detect significant parameters from the system and represent a powerful tool serving as a failure prevention.

Data Analytics/AI

Powua's Analytics module is an effective tool for data analysis. By integrating artificial intelligence algorithms, this module is able to learn from the historical data collected relating to plants, failures and operating conditions, correlating them with external data sources such as those relating to production management, maintenance interventions and maintenance requirements to predict possible failures, identify anomalies and carry out root cause analysis (RCA). This allows for the predictive maintenance of plants and optimization of production processes.