Minimizing unplanned downtime with ML-powered predictive maintenance

World’s leading industrial furnaces manufacturing company builds ML powered IIoT platform to help customers in minimizing unplanned downtime using predictive maintenance

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About Client

The industrial equipment manufacturer offers advanced technology for the engineering & manufacturing of thermal processing equipment.

Challenges

1. Delayed insights for clients into equipment and plant performance machine downtimes: The on-site operations or production team records machine’s and plant’s performance at the end of each shift / day. This data is then manually collated and analyzed to generate machine, plant level reports by connecting other data sources for labor, costs, maintenance etc. This delays timely insight into the performance and throughput. Any corrective actions can be taken only after reports are collated at the end of week. Also analyzing health and every condition alert of each equipment needs drill-down to every detail, such as power supply, cooling system’s temperature, fan, and control boards’ status. Their teams need equipment drawings, spare part lists, and more details for faster analysis, repair, and replacement. Since the entire process was manual and depended on several scheduled inspections/reactive maintenance, ensuring uptime was expensive and time-consuming.

2. Complexity in managing Equipment monitoring software for multiple customers: The company has 50 individual product lines and caters to different customers across the globe. Each customer uses different combination of equipment. Installing and maintaining customized software for each client and their locations separately would make their solution non-scalable. This would also increase overheads of the client services teams.

Solution: Performance and condition monitoring of 100s of machines

With Saviant, the company built an industrial IoT solution and integrated Digital Twins capabilities. Their customers can now see their melt shop overview and drill-down to each equipment detail with ease using virtual view. They can easily access machine performance KPIs like OEE, throughput, quality etc. remotely in time to make any changes in the production plans proactively. Their service teams can access nearly every detail of induction melting equipment from anywhere, anytime.

Digital Twins solution architecture diagram

The system captures equipment data over standard industrial protocols and stores it on edge & cloud for further analysis & alerts. All equipment and their details are virtualized on this platform so that users can touch any equipment icon to visualize and expand each piece.

Saviant created a multi-tenant loosely-coupled architecture that supports the company in creating this scalable platform. This reduced overheads while managing equipment at multiple customers.

Impact

Using the solution, plant managers can now maximize equipment uptime by predicting problems before they occur. With our client’s induction furnaces and state-of-the-art IIoT platform, industrial clientele including Boeing and NASA are increasing productivity and reducing downtimes by monitoring & controlling their machines better.

Impact of iiot platform