Computer Vision Monitoring System
Computer Vision
A visual-plus-audio fusion system that watches and listens to detect faults (cracks, leaks and failing equipment) and raises actionable diagnostics in real time. Deployed for several clients, from HVAC systems to aircraft engine scans.
The challenge
Manual monitoring of cameras and equipment meant faults — structural cracks, leaks, fire risks, failing motors — were caught late, driving downtime and safety risk.
What we built
We built a multi-modal fusion system: a vision model (YOLOv8/CNN) detects cracks, leaks, and fire risks while an audio model analyses spectrograms for hissing, dripping, and grinding. A fusion engine combines sight and sound for confidence and issues actionable diagnostics — deployed across hybrid edge/cloud and retrained on the client's own data in a continuous-learning pipeline.
Results
- Spots cracks in machinery early, before they become failures
- Continuous, automated 24/7 monitoring
- Combined visual + audio detection for confidence
- Actionable diagnostics with recommended actions
- Deployed for several clients: HVAC systems, aircraft engine scans and more
How it works
- 1
Watch: computer vision
A vision model based on YOLOv8 and CNNs analyses camera feeds to spot cracks in machinery early, along with leaks, smoke and fire risks, and other visible equipment faults.
- 2
Listen: audio analysis
An audio model analyses spectrograms to recognise sounds that signal trouble, such as hissing, dripping and grinding, often before a fault is visible.
- 3
Combine for confidence
A fusion engine weighs sight and sound together, so an alert backed by both signals is treated with more confidence than either alone, cutting false alarms.
- 4
Act and improve
Alerts arrive as actionable diagnostics with recommended next steps. The system runs across edge and cloud and is retrained on the client's own data, so it improves over time.
Is this a fit for you?
A similar approach usually pays off when:
- Equipment such as HVAC plant, engines or production machinery needs regular inspection for cracks and wear
- Faults are found by people watching screens or walking rounds
- Downtime or safety incidents follow problems that were visible or audible earlier
- Existing camera infrastructure isn't being used for anything beyond recording
- Sites are spread out, or monitoring is needed around the clock
Common questions
Why combine video and audio?
Some faults are visible, some are audible, and some show up in both. Combining them catches more problems and raises confidence in each alert, which reduces false alarms.
Where has it been used?
For several clients and very different equipment, from HVAC systems to aircraft engine scans. The models are retrained on each client's own data, so the same approach adapts to the equipment being inspected.
Does it need new cameras?
Often it can use existing camera infrastructure, adding microphones or edge devices where needed. A short site assessment shows what's usable.
Where does the processing happen?
In a hybrid of edge and cloud: time-critical detection runs close to the cameras, while model training and fleet-wide analysis run in Azure.
Related reading
- Machine LearningPredictive Analytics: ManufacturingManufacturers and logistics operators sit on more operational data than almost anyone — and use less of it. Where predictive analytics pays off first.
- Machine LearningOperational AI for AviationIn aviation and logistics, AI earns its keep on the front line: coordinating complex schedules and catching faults before they cause downtime.
- Machine LearningThe MLOps Checklist That MattersMost machine-learning projects die in the gap between a promising notebook and a reliable production service. This is the checklist we use to cross it.