Run your machines how they should be running with real-time data on the status of the most important parts of the drivetrain. We help you:
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Through machine learning and electrical signature analysis, SAM4 offers superior fault detection accuracy: over 90% of failures—both mechanical and electrical—up to 5 months in advance. SAM4’s sensors install inside the motor control cabinet, not on the asset in the field, making hazards and location no barrier for installation.
Have your maintenance team get alerts about an upcoming fault, the type of fault, and recommended next actions depending on the urgency of the fault.
SAM4 Health is an AI-powered real-time analytics platform to know in advance when a machine is unhealthy.
Want to use your own systems? Connect to SAM4 through API.
Get alerts on specific upcoming failures weeks to months in advance. These are categorized by type of fault and come with concrete next actions based on level of urgency.
Use SAM4’s real-time pump curve to correct a pump that’s operating outside of its best efficiency point to prevent days or weeks of silent cavitation, recirculation, seal leakage and other problems. Use historical data to permanently raise performance and energy efficiency.
SAM4 includes full power quality and energy analysis for every asset type without the need for additional sensors. SAM4 electrical trends to help you permanently reduce your energy consumption.
Get ongoing personal attention by our team of industry experts, data scientists and maintenance consultants with insight reports. You’ll also receive regular insight reports from our support team to help you make the most of all our features.
Condition monitoring is an important tool in the predictive maintenance of machines. By collecting and analyzing certain signals from motors, developing faults and inefficiencies can be identified, and unplanned downtime can be avoided.
There are a number of different signals that can be taken into account when monitoring mechanical assets. Traditional techniques were mostly based around vibration analysis, but more modern, innovative techniques focus on ESA (electrical signature analysis).
ESA stands for electrical signature analysis. Where other condition monitoring technologies analyze vibrations or oil or temperature, ESA analyzes current and voltage.
It’s a close cousin to motor circuit analysis, or MCA. The difference is that ESA is performed while the machine is running (or “online”), while MCA is performed while the machine is deenergized (“offline”).
Electrical signature analysis is based on the fact that subtle changes in a machine’s operation affect the connected motor’s magnetic field, which then affects the supply voltage and operating current. (For this reason, ESA works equally well on generators.) Using a variety of analytical techniques, ESA can provide a detailed picture of what’s going on across the entire drive train, from motor to transmission to load.
SAM4 analyzes current and voltage sinewaves to provide insights into the condition and performance of electric motors and rotating equipment. It can detect mechanical and electrical failures that occur throughout the drivetrain. The technology is based on Electrical Signature Analysis (ESA) and the hardware is installed inside the motor control cabinet (MCC), not on the asset itself.
SAM4 has three distinct benefits that jointly drive value add:
SAM4 Health is a system to help you prevent unexpected machine failure by detecting electrical and mechanical faults through analysis of current and voltage signals.
Through machine learning and electrical signature analysis, SAM4 offers superior fault detection accuracy: over 90% of failures—both mechanical and electrical—up to 5 months in advance.
SAM4’s sensors install inside the motor control cabinet, not on the asset in the field, making hazards and location no barrier to reliable asset monitoring.
SAM4 Energy is a system that helps you reduce machine energy waste and cut emissions to reach your net zero goals faster.
SAM4 Energy is the world’s first continuous monitoring system for industrial machine efficiency. Our world leading analytics platform diagnoses the causes of energy inefficiency in industrial assets and provides concrete action points to improve on.
SAM4 generates high frequency measurements from inside the motor control cabinet (MCC). The current and voltage measurements are ‘translated’ into a frequency spectrum. In order to detect upcoming failures, different measurements are compared. Deviation from the ‘normal pattern’ may indicate upcoming failures.
ESA can detect and localize mechanical faults in diverse parts of the connected asset.
No, SAM4 is only provided with a MaaS subscription (Monitoring as a Service).
Self-service is not available for SAM4.
Electrical signature analysis (ESA)