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.
The Prague Public Transport Company as well as Wiener Linien are taking a progressive approach to new trends in the field of predictive maintenance, reflecting the increasing expectations of passengers, as well as requirements for efficiency, human resource management, long-term investments, and the digitization of equipment and processes.
A diagnostician receives an alert about a pump anomaly. Before stopping the pump, he checks the possible cause of the fault in the nGuard diagnostic tool. Through remote analysis, he determines that the fault is not directly on the pump, but in the piping upstream of the pump. Based on this, a decision is made to shut down the pump outside of core operating hours and to check the piping.
Neuron Soundware won the ABB Open Innovation Award for Predictive Maintenance. From more than 1100 startups from around the world, Neuron Soundware was awarded as the Best for Predictive Capabilities thanks to its complete solution, including AI platform, product in a box concept, and range width.
The Czech startup Neuron Soundware, which develops solutions for automatic maintenance based on sound and vibration analysis of machines, has not only penetrated abroad, but also the frontiers of space. The technology combining IoT and artificial intelligence is mostly used on machines in factories or on escalators to avoid unplanned downtime.
