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.
The customer required a solution to address the detrimental impact of cavitations in pump equipment on machinery and manufacturing quality. Utilizing NSW technology, anomalous cavitation sound signals were detected promptly, enabling swift elimination of unforeseen failures. NSW technology effectively resolved a problem conventional monitoring systems can’t address.
Efficient port operations rely on the health of mechanical components in material handling equipment. This solution employed certified NSW IoT devices to gather acoustic and vibration data which, together with AI and Machine Learning analysis, provided actionable insights, enabling remote 24/7 monitoring, curbing unplanned downtime, cutting maintenance expenses, and enhancing equipment lifespan, safety, and reliability.
This case describes a solution for overstrained escalator mechanical units which provided real-time uptime reports and remote access. The solution entails producing utilization reports, enabling efficient maintenance scheduling, and providing essential operational insights. Benefit from cost savings through remote monitoring, benchmarking of operation and maintenance effectiveness, and access to vital machine data.
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.
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.
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.
