Who benefits from SAM4 predictive maintenance?
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Preventing downtime at industry leaders worldwide
Hear from ArcelorMittal on how SAM4 predictive maintenance enables the detection of developing asset faults from inside the motor control cabinet.
SAM4 detected 100% of developing faults on a baggage handling system over a 12-month period, eliminating unplanned downtime.
Monitor. Detect. Act. Electrical waveform analysis
Install inside MCC
The MCC is a cheap, safe and convenient place for sensor installation, which can be done within 30 minutes per sensor by your own team.
Traditional predictive maintenance requires on-asset sensor installation, which is complicated if the asset is located in a hazardous environment, an ATEX zone or a hard-to-reach place.
Detect over 9 out of 10 failures
SAM4 uses machine learning, motor current signature analysis and voltage measurements to detect more than 90% of developing faults. Traditional vibration-based predictive maintenance can predict roughly 70% of developing faults.
Failure warnings up to 5 months in advance
Increase electrical efficiency
Because SAM4 measures both current and voltage, it can detect developing faults that cause motors to run less efficiently, motors that need to be rightsized for their processes, and processes that are inefficient by design.
Your company can use these insights to reduce energy consumption.
Proven results in weeks, not years
Installation30–90 min per asset
SAM4 installs inside the motor control cabinet.
Learning phase2–6 weeks
Once installed, SAM4 starts to learn the specific patterns of your assets.
After the learning phase, SAM4 monitors your assets 24 / 7 and sends an alarm when it detects potential failures.
ResourcesSee all resources
Nouryon has signed a framework agreement to implement self-learning technology developed by Semiotic Labs that helps predict when to maintain and replace pumps and other rotating equipment.