Machine Condition Monitoring · AI

Hire your world-leading
engineer. For the
price of software.

MachineVibe.AI delivers interpretable, AI-powered machine diagnostics that match the judgement of the world's best vibration analysts, at a fraction of the cost.

All available approaches force you to choose between cost and trust.

Machine condition monitoring can be addressed by human experts or automated with AI. Both routes work in theory. In practice, each comes with drawbacks serious enough to make industrial-scale deployment impractical.

01 · Human Experts

Expensive and prone to error

Specialists bring real expertise, but they are costly, slow, and fallible. Human performance degrades under fatigue, and availability never scales.

  • Expensive to hire, retain, and deploy on-site
  • Prone to error under fatigue and time pressure
  • Cannot scale across many machines simultaneously

02 · Standard AI

Untrustworthy and data-hungry

Generic ML models offer automation but cannot be trusted in safety-critical settings. They require vast labelled datasets and give no explanation for their outputs.

  • Black-box outputs no engineer can verify
  • Requires large labelled datasets rarely available in practice
  • Poor generalisation across machines and environments

Expert reasoning,
AI optimised.

We replicate the step-by-step workflow of a world-class vibration analyst, then let AI optimise every parameter within physically meaningful boundaries. The result is transparent, accurate, and works with the data you actually have.

Fully Interpretable

Every network layer maps to a known signal-processing step. Decisions are explainable in engineering terms, never a statistical black box.

Automatic Expert Reports

Diagnoses are structured like a professional engineer's report, covering fault classification, severity assessment, and supporting evidence.

Higher Accuracy

Our architecture consistently outperforms both standard deep learning models and human analysts on independent benchmarks.

Low Data Requirements

Physics-informed constraints dramatically reduce the labelled data needed, practical even in data-scarce industrial environments.

Minimal Cost

Automated inference eliminates on-site personnel costs. Scale to hundreds of machines for a fraction of a single expert's annual salary.

Real-Time Speed

Analysis that takes an engineer hours is completed in seconds, enabling continuous, uninterrupted condition monitoring around the clock.

Proven concept.
Ready to deploy.

MachineVibe.AI is built by researchers at the intersection of signal processing, mechanical engineering, and applied AI. The underlying approach is scientifically validated and ready for real-world deployment.

Published in Mechanical Systems and Signal Processing Peer-reviewed and independently validated in the world's leading journal for condition monitoring signal processing.
5+ years of specialised expertise Deep domain knowledge in vibration analysis and physics-informed neural networks, built for industry not repurposed from general AI.
Validated on real-world benchmark data The system outperforms current deep learning alternatives on independent benchmarks and is ready for deployment in real industrial settings.

Ready to modernise your
condition monitoring?

Whether you want a live demo, a technical deep-dive, or just want to understand if MachineVibe.AI is right for your use case. We would love to hear from you.

info@machinevibe.ai

Get in Touch

Fill in your details and we'll be in contact shortly.