Predictive Maintenance Done Right – a Cognitive Approach for Industry 4.0

On-Demand Webinar

Predictive Maintenance has been around for some time now. While Predictive Maintenance is focused on understanding failures, Cognitive Predictive Maintenance is focused on understanding asset behavior and then attributing the pattern changes before the failure. Machine Learning and Anomaly Detection has the potential to completely transform the way industrial asset behavior can be analyzed. Identifying sensor patterns using advanced time series deduction concepts can help understand an asset’s normal working behavior and then identify failure signatures well before the point of failure.

The presentation will focus on real world scenarios where anomaly detection and machine learning has been used to predict failures in industrial assets. Traditional approaches to analytics cannot be applied to IIOT scenarios as the scale and the nature of data is very different. Further, failures are very few, leaving little training data to create accurate predictive models for failures. To ensure that accurate predictive models are created, a new approach is needed which operates at scale and models for asset behavior. In such a scenario, failure becomes a context in the behavior and has a better accuracy since more information is available to train. 


Abhishek Tandon Photograph  Aditya Murukutla Photograph 
Abhishek Tandon
Sales Engineer
Aditya Murukutla,
Product Manager,

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