At present, maintenance is often preventive and time-based rather than based on actual equipment conditions and predictive methods, and this results in unnecessary maintenance costs and causes unanticipated downtime.
Predictive maintenance describes a set of techniques to accurately monitor the current condition of machines or any type of industrial equipment, with the goal of predicting upcoming machine failure by using automated quasi-real-time analytics and machine learning. This approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when needed.
In the Remote Predictive Maintenance solution, the evaluation of vibrations, acoustic signals, temperature, humidity, and further parameters is implemented by an artificial intelligence (AI) algorithm and transmitted to a dashboard via LTE-M or NB-IoT and secure MQTT, alerting the supervisor via a web browser if any maintenance is required before failure occurs.
Renesas' complementary product portfolios of Analog + Power + Embedded Processing + Connectivity work together to deliver comprehensive solutions. Our product experts have developed "Winning Combinations," compelling product combinations that help our customers accelerate their designs and get to market faster.