USE CASE
Enable Predictive Maintenance at the Edge
Prevent costly downtime in remote locations with a secure edge orchestration platform that enables AI model updates and remote asset monitoring for reliable predictive maintenance.
Process Data at the Edge for Faster Predictive Maintenance at Lower Cost
Simplified Edge Data Processing Operations
Reduce predictive maintenance deployment times by 75%, keep AI models up to date, and monitor remote assets from single pane of glass.
Zero-Trust Security
Secure your AI models, data, and access controls, even in remote, unmonitored environments.
Resilient Connectivity
Ensures resilient predictive model execution and seamless updates, even with air-gapped and intermittent connectivity challenges.
Lower TCO
Eliminate costly truckrolls or on-site support and reduce costly cloud bandwidth.
Why Enable Predictive Maintenance at the Edge
Organizations managing critical assets in industries like manufacturing, transportation, and energy face significant challenges with equipment reliability, downtime, and maintenance costs. Traditional maintenance models are inefficient and often lead to unplanned outages, hefty maintenance expenses, and equipment failures. Enabling predictive maintenance at the edge helps organizations identify and resolve potential issues before they impact operations or business continuity, preventing costly downtime and financial losses due to equipment failure.
How ZEDEDA Helps You Enable Predictive Maintenance at the Edge
ZEDEDA empowers organizations to process data at the edge for real-time insights, keep predictive maintenance AI models up to date, and monitor remote assets from a single pane of glass at scale. ZEDEDA’s Zero Trust security model ensures data is protected in insecure environments and its Zero Touch provisioning eliminates the need for expensive onsite resources to keep applications up to date.
Simplified Edge Data Processing Operations
- Deploy, monitor, and manage hardware-aware AI models across disparate geographical locations at scale.
- Upgrade and replace re-trained predictive AI models seamlessly across fleets.
- Remotely manage and update every aspect of device and application operations, eliminating the need for on-site IT staff.
Zero-Trust Security
- Safeguard predictive AI models and machine health data with strong encryption, both in-flight and at rest, even for nodes in unsecured remote locations.
- Ensure only approved and validated applications run on nodes and access shared data through application attestation.
- Enforce granular role-based access controls (RBAC) for all operations, enhancing security and preventing unauthorized access.
- Address edge security challenges, including device loss or theft, loss, unauthorized peripherals, and physical threats.
Resilient Connectivity
- Securely manage device fleets, including predictive AI models and monitoring, in challenging networking environments.
- Maintain resilient AI model execution and seamless updates, even with intermittent or air-gapped connectivity.
- Automatically transition between connected, disconnected, and air gapped states without disrupting operations or requiring manual intervention.
Lower TCO
- Achieve lower total cost of ownership (TCO) at the edge with zero-touch deployment and remote management.
- Reduce hardware and maintenance costs by leveraging commodity hardware.
- Minimize expensive bandwidth usage that results from bringing data back to the cloud by processing data locally at the edge.
Case Study
How ZEDEDA is Helping PeopleFlo Revolutionize Fluid Technology with Intelligent Industrial Pump Systems
Learn how PeopleFlo leverages the ZEDEDA edge computing platform to transform industrial pump systems from passive, stand-alone and disconnected assets into smart, synchronized and connected networks.
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