Neuron Soundware

Turn the seasoned mechanic's intuition into scalable AI for predictive maintenance.

Contact for pricing Czechia

Founded in Prague in 2016, Neuron Soundware brings an age-old concept into the modern era: experienced technicians can often tell when a machine is failing simply by listening to it. The difference is that Neuron Soundware turns this intuition into a scalable, 24/7 AI system.

Features & Use Cases

A veteran maintenance engineer can walk across a factory floor and immediately tell if a pump sounds off. While effective, this expertise is tied to specific individuals who eventually retire or leave, and a single person can only monitor one place at a time. Neuron Soundware uses its nEdge IoT hardware to capture audio and vibration signals from machinery, feeding them into the nGuard cloud platform for analysis. The AI models learn the normal acoustic baseline of each machine, triggering early warnings the moment an anomaly arises.

Compared to mainstream vibration analysis, acoustics offer distinct advantages: microphones can be installed non-destructively, eliminating the need to shut down operations and tear down equipment to mount sensors. Furthermore, sound contains richer frequency spectrum data than vibration alone, making it exceptionally sensitive to specific failure modes such as cavitation, early-stage bearing wear, and valve leakage. It is ideal for monitoring engines, compressors, pumps, and various types of production equipment.

This solution is well-suited for manufacturing, energy, water treatment, and transportation industries operating heavy rotating machinery. It is particularly valuable for aging industrial facilities facing severe skills gaps as senior technicians retire, where replacing entire production lines is impractical. The non-contact nature of acoustic monitoring allows it to be retrofitted onto legacy equipment much more pragmatically than solutions requiring heavy mechanical modifications.

Key Features

nEdge / nEdge Pro: IoT edge hardware for capturing audio, vibration, and other physical signals
nGuard: Web-based analytics platform for processing data and assessing equipment health
AI models that learn each machine's normal acoustic baseline to detect anomalous deviations
Non-contact microphone installation with no downtime or equipment teardown required
Suitable for engines, compressors, pumps, and diverse production machinery
Early warnings and failure mode interpretation
Edge computing processing to reduce bandwidth demands

Pros

Non-contact acoustic installation is exceptionally friendly for legacy equipment that cannot be shut down or retrofitted with traditional sensors
Scales the 'mechanic's ear' to combat industry-wide technical skills gaps
End-to-end hardware and software integration removes the need to piece together custom sensor stacks
Solid European industrial track record backed by real-world manufacturing floor experience

Cons

Complex factory acoustics and background noise from adjacent machines present real challenges, making placement critical
Models require an initial learning period to establish normal baselines, meaning no immediate anomaly detection on day one
As a smaller company, regional technical support availability should be verified beforehand
Pricing is not publicly disclosed; requires hardware procurement rather than a pure software subscription

Use Cases

Early detection of bearing wear in rotating machinery such as compressors and pumps
Continuous health monitoring for non-stop production line equipment
Condition monitoring for aging legacy assets where traditional vibration sensors cannot be mounted
Transitioning maintenance schedules from calendar-based to condition-based to minimize unnecessary downtime
Remote diagnostics and failure mode classification for machine anomalies

Editor's Note

Editor's Note: This is a particularly pressing challenge for modern manufacturing. The biggest pain point isn't just aging hardware; it's the mass retirement of experienced personnel who instinctively know when a machine is failing—knowledge that was never written down in standard operating procedures and walks out the door on their final day. Using acoustic AI to preserve this expertise is the right direction. For facility managers interested in exploring this, I recommend starting small: pick three critical assets you know best and have experienced the most trouble with as a pilot project, and use your historical failure data to validate whether the system can catch them. Don't roll it out across the entire factory all at once.

FAQ

How can acoustic analysis work in a noisy factory floor?

This is the most common and valid concern. In practice, it is handled through a combination of close-range microphone placement, AI models that learn specific machine frequency signatures rather than overall volume, and signal processing techniques to filter out background noise. However, in extremely chaotic acoustic environments, performance can be affected, making an on-site pilot test essential before full deployment.

How does it compare to traditional vibration analysis (e.g., Augury)?

Vibration analysis is the mainstream, highly mature methodology for predictive maintenance. Acoustics shine in their non-contact installation and richer frequency spectrum data. If equipment can be shut down for sensor mounting and budgets allow, vibration ecosystems are more comprehensive. However, for legacy machinery that cannot be taken offline, acoustic deployment offers significantly better flexibility. The two methods can also complement each other.

How long does it take to see results?

The AI must first learn the baseline normal state of the equipment, which typically takes anywhere from a few weeks to several months depending on the complexity of the operational cycle. This is standard for all condition-based monitoring solutions—do not expect it to catch faults the day after installation.