Fix It Before It Breaks: Incremental Learning for Predictive Maintenance

Big data drives big decisions in smart factories. Connected, automated machines produce streams of real-time data, which artificial intelligence algorithms process into actionable knowledge. Producing that knowledge often requires significant time with batch processing that takes hours or runs overnight. This lag time between data and decision creates supply chain and maintenance inefficiencies: supplies must be stockpiled to prevent shortages and machines are serviced on a time-based rather than as-needed schedule. Presenting near real-time knowledge to decision makers enables better decisions and provides the competitive advantage of increased manufacturing efficiency.

The complexity of a supply chain or manufacturing process makes it difficult to manually develop the accurate models required for knowledge creation. Machine learning algorithms build these models automatically. Many currently deployed machine learning solutions use the Lambda Architecture, a hybrid of near real-time and batch processing. Such systems process streams of data in near real-time using a periodically updated model. But if the real-time data signals significant new trends, the model may not recognize or respond to those trends until the next update.

A new class of machine learning algorithms increases responsiveness and accuracy by dynamically updating their models in near real-time. These incremental or online learning algorithms process the incoming data into knowledge and then feed that knowledge back into the model. Updating the model from the data stream has several advantages: fewer copies of the data, which increases security; elimination of the time and cost of model redistribution; ability to handle data sets that exceed system memory or storage capacity; and the opportunity to apply machine learning in isolated environments, where batch processing resources are not available.
Recorded at Big Things Conference 2019.