Anomaly detection
Flag unusual machine behaviour even when there is no failure history to learn from yet.
Back to all solutions
PREDICT EQUIPMENT PROBLEMS · PREDICTIVE MAINTENANCE
Know before
it fails.
Most sites find out about a failing pump, motor or gearbox when it stops. The warning signs are often already in the data — vibration, temperature, current, sound and maintenance records. Machine learning can learn what normal looks like and flag the drift early enough to plan the repair.
01 / IN PRACTICE
Three examples,
problem to result.
Each example is a demonstration on illustrative data, laid out the way a real project runs: the business problem, the data, the AI, the result and the potential value.
01 / PREDICTIVE MAINTENANCE
Could this pump have warned you before it failed?
- BUSINESS PROBLEM
- A critical pump fails a few times a year. Each failure stops the line, and the repair is always a rush job.
- DATA
- Vibration, bearing temperature and motor current already logged by the PLC, plus two years of maintenance records showing when and why it failed.
- AI
- A model learns how the readings drift in the weeks before a failure and turns them into a single health index with an estimate of the time remaining.
- RESULT
- A service window suggested around twelve days ahead, with the likely cause, sent to the maintenance planner to review.
POTENTIAL VALUERepairs planned into scheduled downtime instead of lost production — and fewer unnecessary routine services.
Predictive maintenanceDEMONSTRATION · ILLUSTRATIVE DATA
HEALTH INDEX0.52LIKELY CAUSEBearing wearSERVICE BYWed 15 Oct
SERVICE LIMIT
WORK ORDER DRAFTED FOR REVIEW ±4 days
02 / CONDITION MONITORING BY SOUND
Could you hear a bearing failing weeks before it seizes?
- BUSINESS PROBLEM
- Fans, compressors and motors in hard-to-reach places are only checked on rounds, so faults are often found late.
- DATA
- A low-cost microphone or accelerometer beside each machine, recording a few seconds every few minutes.
- AI
- An anomaly model learns the sound of healthy running and scores how far each new recording departs from it, recognising patterns such as bearing wear.
- RESULT
- An alert when the score crosses a threshold, with the frequency band that changed and a suggested inspection date.
POTENTIAL VALUECondition monitoring on machines that never justified a full vibration-monitoring system.
Condition monitoring by soundDEMONSTRATION · ILLUSTRATIVE DATA
FREQUENCY · LAST 60 MIN
ANOMALY SCORE
CONDITION
Normal running Bearing wear likely 0.91
- 3.2 kHz tone rising
- Matches bearing wear pattern
- Inspect within 14 days
WORK ORDER RAISED BEFORE FAILURE ~14 days notice
03 / STOPPAGES, JAMS & LEAKS
Could you know about a jam before anyone walks past?
- BUSINESS PROBLEM
- Short stoppages and compressed-air leaks go unrecorded, so nobody knows how much time and energy they really cost.
- DATA
- Audio from a microphone on the line, plus the stop and start signals from the PLC where they are available.
- AI
- A sound-event model picks out jams, stops and the hiss of air leaks from the background noise and logs each one with a timestamp.
- RESULT
- An alert for each jam as it happens, and a log of every leak and short stop — without recording conversations.
POTENTIAL VALUESmall, hidden losses made visible, so the worst offenders can be fixed first.
Stoppages, jams & leaksDEMONSTRATION · ILLUSTRATIVE DATA
conveyor_jam 0.96air_leak 0.91
- Background · normal running
- Infeed jam — line lead alerted
- Compressed air leak — logged
LINE LEAD ALERTED · NO SPEECH RECORDED 0.5 sec
02 / AND MORE
Other ways to
predict problems.
Fleet & mobile plant
Use telematics and fault codes to plan servicing across vehicles, forklifts and mobile equipment.
Tool wear
Estimate cutting-tool condition from spindle load or sound, and change tools at the right time.
Thermal monitoring
Spot hot bearings, panels and connections from thermal images or temperature trends.
Running at the edge
Run models on small devices beside the machine, where connectivity is limited or data must stay on site.
Critical spares
Use predicted failures to hold the spares that matter, rather than the whole catalogue.
03 / BEFORE WE BUILD
What we look at
before building anything.
History with timestamps
Months of sensor readings at a useful rate, ideally covering some of the failures you care about.
Records of what happened
Maintenance logs, work orders or downtime records that tell the model what the readings led to.
Engineers in the loop
Time with the people who know the machines, so the model respects the physics and its alerts make sense.
A safe way to act on it
Alerts go to a person to review. Nothing changes on the plant automatically.
04 / HOW WE'D START
Idea → evidence
→ investment.
You never commit to a large project on a hunch. Each step answers whether the next one is worth taking — and “no” is a useful answer too.
- 01
Free conversation
30 minutes about a process, machine or business problem. No technical sales pitch.
- 02
AI Opportunity Review
A short paid review of the problem, your data and systems, ending in a roadmap and a recommendation.
- 03
Proof of concept
One problem tested on your real data, with a clear Go, Modify or Stop at the end.
- 04
Build & improve
Only if it works: a practical system, deployed, monitored and refined.
05 / PREDICT EQUIPMENT PROBLEMS
Finding problems
after the failure?
Tell me about the machine and the data you already record. In a free 30-minute conversation we can talk through whether your data is likely to show the warning signs, and what a small proof of concept would look like.
Independent · North Yorkshire · working across the UK