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

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

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

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

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

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

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

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

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

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

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