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Predictive Maintenance

Predictive maintenance is a maintenance strategy that uses data and analytics to predict when equipment is likely to fail and schedule maintenance before the failure occurs. The goal of predictive maintenance is to prevent equipment failures and unplanned downtime, which can be costly and disrupt operations.

Predictive maintenance typically involves collecting data from equipment sensors and other sources, such as maintenance records and operational data, and analyzing this data to predict when equipment is likely to fail. This can be done using a variety of techniques, including machine learning algorithms, statistical modeling, and data visualization tools.

Once equipment failure is predicted, maintenance can be scheduled in advance to prevent the failure from occurring. This can be done using a variety of techniques, such as replacing parts before they fail or performing regular inspections and maintenance on equipment.

Predictive maintenance can provide a number of benefits to organizations, including reduced equipment downtime, lower maintenance costs, and improved equipment performance. It is often used in industries such as manufacturing, transportation, and energy, where equipment failure can have significant consequences.

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.


What is the problem?

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.


How do we solve this?

Implementing a real-time data platform for processing and transforming IoT data is crucial for improving uptime, reducing maintenance costs, and managing risk. InfinyOn offers a comprehensive set of tools that enable building data streaming pipelines for predictive maintenance and analytics:

  • 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 allow for cleaning, transforming, filtering and aggregating data without the need for moving data in and out of the streaming platform
  • The SmartModule Hub allows for the sharing and reuse of data transformations across an entire organization, simplifying the data pipeline development process

Reference Architecture Diagram

MQTT to Postgres Reference Architecture Diagram

And follow our tutorial for how to build MQTT to SQL Data Pipelines