1. Driving higher profitability through Machine learning
Lessons and insights
Maximo User Group, Nov’17 Ben Mabbott – LFG Operations Infinis Neil Douglas – I/T Infinis George Velimachitis – Eng

2. Infinis Renewables… £166m in annual revenue,
1.7TWh from 121 UK sites, operating 24x7
Over 350 engines running at any point in time
[Avg.] MTBB 226h, MTTR 9.6h
£8m profit lost due to (a) LFG depletion & (b) 11d outage on the two largest sites

3. Availability & Reliability
Infinis’ challenge…
Downtime 
Lost Revenue
ability to answer …
Can we predict engine failure?
How could we prevent it ?
When is it best time to intervene ?
How long will it be down for ?  
Availability & Reliability
Profit  
Maintenance
Regime 
Direct & Indirect costs

4. The benefits… Transitioning from “reactive” to “proactive”
downtime risk and downtime duration
utilization of installed engine capacity
workforce efficiency
5% improvement in MTTR & MTBB = £1m income improvement

5. Our vision… Predict next engine outage and fault type likelihood
Predict LFG generation outlook for the next 7-14 days
Optimise Production schedule & timing of Maintenance
Assist front-desk and field engineers with best action plan

6. The challenge The approach SCADA & Maximo data Unchartered territory
Constrained budget, limited skills & knowledge
The approach
Small steps, Clear Goals, Ring fenced time & budget
Design & Run experiments

7. Can we predict engine failure ?
What we’ve learned…
Data Forensics. Interrogate !
Engines have personality !
Sensors can be a challenge
Success ? Think again, validate !
We can differentiate between operating states incl. failures, by looking at existing historical data

8. Can we intervene in advance and before engine failing ?
We can get advance warning of up to 6h from engine failing Model is good enough to trial on operational decisions
Data collection and classification process improvements

9. Can we predict the next outage and type of failure ?
Feasible but more work is required. Improvement in subsystem/component level data collection
Data requirements are better understood

10. Conclusion Machine Learning is transformative
Think big, start with small focused steps
Operational maturity affects adoption rate
Think “Continuous Process”

11. www.peluk.org +44(0) 203 356 9629 info@peluk.org