Condition Monitoring Review Using Relialytics Semi-Automated Support Service September 2017

Condition Monitoring Data Many condition monitoring review tasks can be automated Why pay engineers to review reports from labs and condition monitoring providers when these tasks can be automated and subsequent actions raised directly into the CMMS? Use computers and AI in areas where there is no need for human review

How does this fit within the current business process? Data Generation by Maintenance Execution by CM Monthly Report / Deliverables Generation of Activity / Work Order Data Analysis by Maintenance Execution by Site Work Orders Laboratory Lubrication Vibration etc Manual Analysis Site based External / Contract Manual Generation Site based External / Contract Manual Site Based Maintenance Trades / Technician Trades / Technician Current CM Process RELIALYTICS SEMI-AUTOMATED SYSTEM Automated generation of: Monthly Report Work order flat file for review Site Work Orders Laboratory Lubrication Vibration etc Automated / Uploaded Work Order Generation Machine Learning System Trades / Technician Trades / Technician Continual Improvement of knowledge and feedback for ConMon Providers Relialytics CM Process

Oil Analysis Project How can we: We looked at “lubricant” sample data: Improve efficiency of CM data review? Better utilise data mining industry spend $10’s of millions generating? We looked at “lubricant” sample data: Increasing workload to analyse samples, provide corrective actions and initiate work orders Many only take action once the damage is already done (“C” samples) Effective analysis has already been completed – why do it again? Reduce data double handling Shows the increase in samples being taken on a site from early 2000s until now. Shows increase in workload Adam is handling about half of this workload at the moment.

Learning – Historical Analysis / Annual Report ID Segment Comments % Samples 1 Wear materials (copper, lead, iron (& PQ index), magnetics etc) present in oil. Viscosity normal. Some samples with oxidation and / or nitration and / or sulphation issues. 27.3% 2 Viscosity normal / acceptable. Wear materials in acceptable range. All other test results appear acceptable. 26.8% 3 21.1% 4 Oxidation increasing & slightly high to high. Some samples with slightly high to high acid no’s. Some samples with wear materials (copper, lead, iron) present. Some samples with oxidation and / or nitration and / or sulphation issues. 16.2% 5 All test results appear to be normal. 6.1% 6 Oxidation issues detected. 0.9% 7 Viscosity slightly low. Fuel dilution appears to be present. 0.4% 2 1 3 4 5 6 7 GENERAL NOTES: Network modularity investigates communities / clusters of samples. Too many engine samples for one network. Split into two networks (older and newer units) to determine whether any difference in engines between older and newer units. 1833 samples in this older unit network The 7 clusters / communities identified above represent 99% of those samples Only one grade of oil used The shape of the network is very different to hydraulics and transmissions. In this case there is a dense central cluster but also two sizeable peripheral clusters (1 and 4) with many samples showing issues with wear materials and oxidation. Statistical analysis of graph shows Oxidation is main issue identified in the samples which act as the main connectors in the network / graph Will have better diagrams once have finalised networks – this is the major job over the next couple of weeks. Will only be saying that this analysis helps review 1000’s of samples concurrently – good for annual reviews or comparing single components.

Learning – Historical Analysis / Annual Report Quick comparison shows that Unit 3 has less samples exhibiting oxidation. May trigger a more detailed investigation as to why. Unit 1 232 samples Unit 2 187 samples Unit 3 185 samples