Explore the capabilities of nShield analytics platform
Enjoy real-time visibility from any device
Use the Dashboard to get real-time assets visibility – condition and monitoring status across all your locations and facilities from anywhere. Quickly filter machines with alerts to plan maintenance action and minimize the risk of unplanned downtime or critical damage.
Add new machines for monitoring in 30 seconds
Quickly onboard a new machine for monitoring with the intuitive wizard: Name your machine – so you can quickly identify your asset. Assign facility – so you can easily refer to the asset location with colleagues. Select machine category – to automatically get the right configuration template. Assign nBox – activate your nBox for the new machine.
Keep your machine information organized and up to date
Start your predictive maintenance program by capturing assets' details. Combine machines' static information like name, location, model, or last service date with real-time status and failure prediction alerts to improve OEE.
Machine condition history
Understand machine health with current and historical events
Improve your maintenance program with easy-to-access insights into machines' maintenance logs, alerts, and real-time failure warnings. Compare data of different machines or periods and perform failure analyses.
Email and SMS notification
Provide instant alert notification for your reliability teams
Set email and phone notification to keep machine operators, plant managers, or reliability teams up to date, so they do not miss machine failure indications. Inform the right personnel in the right location as machine condition, or other details change.
Machine component level diagnostics
Analyze trends per individual machine component
The monitoring of complex machines with multiple components requires the installation of several sensors. Use sensor level drill-down view to understand component level health.
Analyze audio recordings in detail
Access all recordings in real-time
Listen, evaluate, and add context to machine recordings by identifying and labeling essential sounds. We use this context and retrain AI models to improve failure detection accuracy. View recorded data as a waveform or spectrogram, quickly zoom on a specific time range of interest.
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