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Automotive industry: We got you covered
Have each part of your production protected. Boosts the fleet efficiency by predictive maintenance.Contact us
Automotive industry leads automation in production.
Don’t stay behind.
Even a failure of a non-critical asset can lead to thousands Euro losses if missed and lead to collateral damage.
That is why we deliver digitisation of maintenance to automotive industry to contribute to the overall production efficiency.
Maximize the factory uptime
with award-winning AI-based monitoring system
Complete 24/7 information about your asset condition
Get early warnings on upcoming failures so you can act early
Optimize maintenance schedule. Control the future.
Where we helped
Neuron soundware solution for machine health monitoring successfully detected faults on air compressors that would be difficult to detect by other diagnostic methods. This saved the production of car rims for a 50-million per year car wheel producer.
„This Neuron system is a vital tool that will be very helpful in the future for Maxion“
“The cost savings of using the predictive analytics and alert detection alone will more than pay for the cost of the service not to mention the costly rental fees and downtime that is accrued when compressors go down. Mieszko and his Team provide a great service and product that is well appreciated by the Maxion Team.”
Industrial Control Specialist
Read more about predictive maintenance use cases
by Neuron soundware
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Unique AI-based machine monitoring of paint recycling machines
Explore the challenge of IIoT monitoring in an explosive environment.
Successful diagnosis of a pump fault using sound and AI saves the day
Learn more about how Neuron soundware proved its worth in potato chip production and does not cause increased wear.
Neuron soundware technology is used in various applications across different industries using the AI
to process the sound from machines and deliver information saving production and maintenance costs.
For diagnosing machine condition we use machine learning algorithms and notify you in case of malfunctions.
Via monitoring sound of machines we help you increase your machine uptime and discover your capacity reserves.
The AI learns your machine standard behaviour patterns so we can recognize the initial signs of failure.
The unique combination of AI, ML and sound can be leverage for process optimization.
To protect entire machine parks, we focus on individual assets and components:
- Compressors, electric motors, pumps, gearboxes, bearings, fans, shafts
- Conveyors, robotic arms, lifts, generators, converters
Ready to take your production
to the next step of digital?
Uniqueness of NSW solution
World’s Top 5 AI
Deep machinery knowledge
All insights you need to see.
At one dashboard.
Red. Green. Orange. Three colours that will make your life easier.
Easy to understand visualizations as well as instant notifications will help you to better understand you machines. See how.
To make data driven decisions we provide you all machine data processed to easy to read information from any device
Identify bottlenecks through machine health fleet management
All machines at one management dashboard. Uptime and downtime over all assets to help you improve production efficency.
Transparent machine history at one look
Investigate whether the machines were working according to the plan. Check the detected alerts.
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.
Wir arbeiten mit Kunden in der ganzen Welt
Do not postpone the decision.
You can start saving from today!
Across a wide range of sectors, it is not uncommon to see 30 to 50 percent reductions in machine downtime, 10 to 30 percent increases in throughput, 15 to 30 percent improvements in labor productivity, and 85 percent more accurate forecasting.
Implementing advanced analytics can reduce warranty costs by 15 to 20 per cent. For a large OEM Group, this could mean savings up to 200 milion pounds per year.