Case studies in Predictive Maintenance

Discover our case studies and success stories

Welcome to Neuron Soundware’s Case Studies Hub, where you can explore our case studies and success stories. Dive into inspiring narratives showcasing how our AI-driven sound analysis technology is revolutionizing industries. Discover real-world transformations and the remarkable power of sound analysys in our case studies and success stories.


Wind energy has become an increasingly vital component of the global push towards sustainable and renewable energy sources. As the number of wind turbines continues to grow, ensuring their efficient and uninterrupted operation is of paramount importance. One key aspect of this is the implementation of predictive maintenance techniques, which can help identify potential issues before they lead to costly downtime. Neuron Soundware (NSW) has developed a method for predicting and preventing mechanical failures in wind turbines based on the use of sound emission analysis and other physical parameters of wind turbines.

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.

A Neuron Soundware solution was deployed for detecting faults in pneumatic door components of trains, as a result of high penalties for broken train doors which prompted the exploration of preventive measures. Read the expanded case study here.

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.

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.

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.