The asset management landscape is innovating with Predictive maintenance that predicts the future performance of a component or machine. This is made possible by assessing the extent of deviation or degradation of a system from its expected normal operating conditions. The science of predictive maintenance is based on analyzing failure modes, detecting early signs of wear and aging, and fault conditions. Industrial automation in the manufacturing industry has created a growing demand for predictive maintenance solutions.
The primary problem with predictive maintenance is collecting structured consistent data that AI systems can use effectively. Predictive Maintenance relies on collecting large amounts of sensor data, cleaning and transforming data and then loading it into an analytics platform.
The adoption of a real-time data platform to process and transform IoT data is essential to increase in uptime, reduce maintenance costs and manage risk. Infinyon provides a set of tools to enable building data streaming pipelines for predictive maintenance and analytics in general:
- InfinyOn Cloud real-time data platform
- MQTT connector to collect data from various sensors
- SQL connector to load transformed data into SQL-compatible server
- SmartModules to clean, transform, filter and aggregate data without the need to move data in and out of the streaming platform.
- SmartModule Hub to be able to reuse data transformations throughout the whole organization
Reference Architecture Diagram
And follow our tutorial for how to build MQTT to SQL Data Pipelines