To ensure uninterrupted car window production, a European automotive supplier employed NSW technology to monitor critical equipment—oil-injected rotary screw compressors—using IoT devices and non-intrusive sensors. AI and Machine Learning assessed acoustic data in real-time to provide early alerts for potential failures, allowing prioritized inspections and cost reduction. Read the expanded case study here.
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.
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.
