Predictive Maintenance AI

Optimizing maintenance with AI by predicting unplanned downtime

Challenge


Research from Gartner shows the cost of downtime of equipment can skyrocket as high as $540,000 for every hour assets, workers, infrastructure, systems or networks are unavailable. A total of $647 billion is lost globally each year due to machine downtime.

One of the main challenges in transportation is to reduce costs and delays, while maintaining or improving current safety levels. Existing methodologies are time and use-based, lack accuracy, and do not account for statistical variations in component and subcomponent systems.

Failure of structural components can have catastrophic consequences.

A majority of the direct and indirect maintenance costs are the consequence of decisions taken during the initial maintenance program development.

Solution


Our proprietary AI model for Predictive Maintenance, is robust & can be used across industries for identifying RUL (Remaining useful life) of a machine component. Actionable insights provided by the model help the maintenance team and insurer to plan interventions and minimize impending risks. The model identifies top influencers which are strong indicators for maintenance and insurers to manage risks. This model can process data points from IoT sensors, SCADA systems, and historical usage data to build a custom deep learning model to predict specific failure patterns & time.