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.
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.
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.
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.
