Neuron soundware was selected from more than 600 solutions from around the globe, to gather with the TOP16 Internet of Things Innovators 2021 at the 12th IOT Innovation World Cup® pitch and award ceremony, at the Hannover Messe Digital Edition to be then selected by the tech-savvy experts’ jury as one of the world´s TOP 5 Internet of Things Innovators 2021.
Prague subway transports a million passengers every day and the escalators are essential. So the mechanical components of the escalators are constantly used and worn. The operator needs to know when maintenance and replacement of the damaged component are required to avoid their failure. Neuron soundware has a predictive maintenance solution for such a problem.
What inspired the founders of Neuron Soundware to focus on this area, and how does your AI-driven technology offer a unique approach compared to traditional methods of maintenance
Many customers ask us how artificial intelligence works in detecting audio anomalies. We have prepared an article with an illustrative example of escalator monitoring.
Microphones and structure borne sensors provide the operator with not just the ability to listen to the components off site, it also monitors the wind turbine 24/7 using anomaly detection.
Neuron Soundware detected an impending fault in the piston helium compressor for hardening gearboxes in the automotive industry.
In March 2021 our AI detected an impending critical fault in the piston compressor, which later culminated in machine downtime. Early detection of this fault represents a concrete result and proof of how machine learning algorithms can assist in predictive maintenance.
In pursuit of production continuity, a leading wheel rim manufacturer sought a solution to monitor vital air compressors using IIoT devices and AI-driven anomaly detection.
By analyzing sound data, this end-to-end system provides real-time insights, enabling early intervention, asset prioritization, and cost reduction while optimizing workforce efficiency.
AI-driven NSW solution helps to prevent unplanned downtime and decrease repair costs of cogeneration units (CHP).
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.
In the context of a European automotive manufacturer, the use of a piston helium compressor in transmission gear hardening is vital for production. This study addresses the challenge of maintaining uninterrupted operations in the tempering furnace, as equipment failure could lead to production delays and scrap generation. Previously, entire compressors were replaced due to critical incidents. The proposed solution employs IoT devices and non-intrusive sensors to gather and analyze acoustic data. AI and machine learning algorithms assess compressor sounds, promptly detecting deviations from the norm as anomalies. Benefits encompass early detection of potential failures, real-time asset monitoring, preemptive alerts, streamlined inspection prioritization, and reduced costs tied to failures and scrap.
