What is Your Machine Saying?
Voltonix Presents SigSense: The Most Effective, Affordable Way For Device Manufacturers To Create Actionable Insights, Preventative Maintenance Programs, and
–Finally– Predict Failures.
Every Component has a Signature
Whether it’s a stepper motor or an LCD screen, each component has a unique electrical voice. By learning what normal operation looks like, we can detect when things are getting out of whack.
ATM Cash Dispense
The expected signature (green) is dramatically exceeded by the trouble signature (red). Increased friction could be caused by dirt, moisture or an aging motor. The algorithm can find out.
Calibration Test
Ensure that teams are following proper procedures by monitoring test and calibration processes are happening when they are supposed to.
Power Outage!
Monitor your deployed devices in real time. Power quality, temperature, humidity and CO2 are all visible from a single dashboard. Alert parameters are set easily set for reactive response teams.
Putting the Data To Work
Machine learning algorithms crunch and compare all the signatures to create actionable intelligence in three stages:
Utilization
Initial insights will identify how the instrument is being used, how much usage costs per print, run, test or any metric you determine. Alert marketing teams if customers are maxing out and need to expand their investment.
Alerts
Identify if a machine has failed, if a site has lost power, and whether an individual component (like a pump) needs to be replaced. Check if temperature, humidity, and CO2 meet specifications.
Predictions
True proactive maintenance through predictive failure detection. Anticipate failures for proactive parts replacements or preventative maintenance. Reduce downtime and increase reliability.