A Smarter Way to Monitor Your Equipment with IBM's Asset Monitoring and prenode Edge AI

In partnership with IBM, we helped them to overcome data challenges and enhanced asset monitoring with our decentralized AI solution, prenode mlx. By integrating mlx into IBM’s existing infrastructure, our joint showcase, Hammer-Rig, successfully addresses privacy concerns and data silos while leveraging federated machine learning to achieve accurate predictions of overheating events. Through this collaboration, IBM experiences significant positive business impacts. We are confident that our cooperation will pave the way for an integrated AI solution in the future of the smart manufacturing industry.

Challenge

Many software providers, including IBM, want to enrich their products with machine learning (ML) to deliver AI-based digital services that meet the evolving needs of businesses and customers in today's digital landscape.

However, ML needs enormous amounts of data that cannot easily be accessed because customers are unwilling to share their data due to privacy or high data volumes. In addition, companies often face the challenge of data being distributed across silos, making it difficult to effectively use all the data for ML. It causes knowledge of silos and delayed forecasts.

To overcome data challenges and enhance their IBM Maximo® Monitor portfolio (a remote asset monitoring application) with AI capabilities, IBM actively pursued a technical solution.

How we helped

To resolve these challenges, our decentralized AI solution, prenode mlx, offers a comprehensive infrastructure for on-device machine learning, enabling the training of models on federated and isolated data sets. This solution allows training ML models without sharing data with a central entity and keeps the data on edge at individual sites to ensure privacy, security, and accurate predictions.

In collaboration with IBM, we developed a Hammer-Rig showcase where mlx seamlessly integrated into IBM's infrastructure, demonstrating its capability to predict overheating events by analyzing data from multiple pumps. Our decentralized AI software analyzed sensor data from each individual rig, accurately predicting overheating events and sending the predictions to IBM Maximo Monitor platform through IBM Cloud. This enables machine operators to quickly receive warnings when an overheating event is predicted.

We successfully integrated prenode into our ecosystem running on Red Hat OpenShift and like to see them thriving. Together, we bring future-proof technology where data arises. This leads to a great added value for clients. We truly enjoy cooperating with prenode! - Hans-Joachim Köppen, Business Partner Ecosystem and Innovation at IBM Watson Center Munich

Results

The implementation of our mlx solution delivers significant business advantages for IBM and valuable benefits for their customers.

Firstly, it improves asset monitoring capabilities by enhancing prediction accuracy and reducing prediction time through decentralized AI. This empowers machine operators to respond promptly to predictions, mitigate potential damages, and enhance maintenance practices.

Additionally, the decentralized approach promotes knowledge sharing and collaboration across multiple devices, which supports IBM's development of robust federated ML models. Importantly, prenode mlx has effectively tackled the critical challenge of data security, guaranteeing the protection and confidentiality of sensitive information.

The Hammer-Rig showcase can be seen in the IBM Watson Tower in Munich.

Company:

IBM Watson Center Munich

Description:

IBM integrates technology and expertise, providing infrastructure, software (including market-leading Red Hat) and consulting services for clients as they pursue the digital transformation of the world’s mission-critical businesses.

Founded:

1911

Industry:

Information Technology

HQ:

Munich, Germany

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