GE Intelligent Platforms Optimizing equipment health and processes with GE Data Analytics Software Andre Badenhorst Solution Architect Team Leader (MEA) Mining & Metals
The Industrial Internet at GE >5,000 Using data to improve products >3,000 field engineers using the data to solve problems >1,000 Monitoring data PEOPLE >12PB of data from every machine built or retrofitted since 1960 >5TB of machine data collected every day >800 new developers working on insights and visualization DATA >7000 GE gas turbines >15,000 GE locomotives >25,000 GE aircraft engines MACHINES
Logistics Grinding Mill Flotation Smelting Utilities Shipping 3
Logistics Grinding Mill Flotation Smelting Utilities Shipping 4
Implications You are not gaining value from that data. Losing opportunities to: Decrease energy costs Decrease unplanned downtime costs Decrease maintenance costs 5
Implications You are not gaining value from that data. Losing opportunities to: Increase recovery Increase throughput Increase availability 6
Implications - why Because the data is: Un-collected Un-correlated Un-aggregated Un-contextualized Un-usable 7
Next Step What s in your data? Demo 8
Mining and Metals Landscape Need Better Productivity (same throughput at lower cost) with existing assets Lower Margins Lower Ore Grades Lower Capital Budgets Skill Shortage Aging Assets 9
Problem: Change is ambiguous in complex environments Are hard alarm limits proactive? 10
Solution: Use pattern recognition to normalize a Multivariate Problem
Equipment Condition Reactive to Proactive Maintenance Normal operation 100% Events and minor damage E E 80% Proficy 60% A 40% Machinery protection Alarm 20% 0% Machinery protection Trip Life T F Functional failure 12
Predictive Advisories Enabled by Modeling Related Sensors Stand-alone equipment protection Temperature Alarm / Trip Temperature Alarm Hi Dynamic Bands SmartSignal MaxxMine Alerts / Incidents Pressure Δ Speed Flow Normal operation Few false alarms Early stages of damage Alerts before traditional alarms 13
Extending Detection to Diagnosis Sensor Data Predictive Model Tag Level Advisories Something Has Changed 14
Extending Detection to Diagnosis Sensor Data Predictive Model Tag Level Advisories Something Has Changed Component Component Component Failure Diagnostic Pattern Fit Localized Diagnostic Guidance This is the Apparent Cause 15
Extending Detection to Diagnosis Sensor Data Predictive Model Tag Level Advisories Something Has Changed Component Component Component Localized Diagnostic Guidance This is the Apparent Cause Dynamic Priority Assignment / Escalation This is Urgency & Progression 16
Industry Overview 160+ Units 4000+ Assets 100k+ Sensors Some Industry Proof Points: High customer satisfaction:+95% of customers renew contracts each year Proven ROI in various industries across different asset bases: Typical ROI 6 9 Months Major Oil and Gas Company: Trial delivered over $5 MM in first six months. $10 $15 MM estimated annual savings for Power Generation Company - More Than $5 Billion in Assets Under Management - $10 s of Millions in Annual Avoided Costs for out Customers 17
Detection: Decline in pump efficiency FIND Pump efficiency (relative) dropped from ~70% to ~49% over a 4-month period Negative residuals for pump hydraulic efficiency and positive residuals for pump total power ROOT CAUSE Internal pump erosion, leading to the loss of hydraulic efficiency VALUE Early detection led to corrective maintenance, avoiding an emergency shutdown to the change pump. Increased throughput. 18
Success Story: Gearbox temperature changes detected FIND Increased gearbox lube oil temperature from ~55 C to ~70 C Increased gearbox bearing metal temperature from ~70 C to ~90 C ROOT CAUSE A wire had come out of a contactor at a termination point, preventing the lube oil pump from circulating lube oil through the cooler. In addition, the gearbox low oil pressure switch failed. VALUE Early detection enabled corrective action, avoiding catastrophic damage to the gearbox. 19
Basis for process benefits: Variance reduction allows processes to operate closer to limits or constraints Source: ARC Advisory Group 20
PID control loop monitoring optimizes process performance CHALLENGES Manual, out of control loops Sub-optimal process stability Sub-optimal performance OPERATIONAL RESULTS 90% of loops in automatic 90% of loops in control 100% ROI in less than 6 months BEFORE AFTER Dynamic operations for control loop performance monitoring allows for higher process stability and performance 22
PID Loop Monitoring Monitoring the performance of thousands of control loops Support control loop performance management within enterprise model context Analyze control loop performance and control elements Prioritize loop performance identify worst performing control loops and filter according to loop type, section, etc. Track control loop parameter changes 23
PID Loop Health Reports 24
Web Reporting Process Performance Monitor & Track Process & Solution Performance Key Views: Power/Load Curve KPI Trends Particle Size Distribution; Density; Specific Energy; Feedrate (Throughput) 25
Process optimization Milling circuit Feed rate before Feed rate after Stabilization and 5% increase in throughput Improved grind quality Lower energy Asset life extension
Grinding Circuit Solution - Power/Load Before and After Proficy optimizes energy usage APC Off APC On 27
Drying circuit improves production and productivity BEFORE CHALLENGES Changing set points Incorrect temperatures due to bad control Process uncertainties and complexities OPERATIONAL RESULTS 25% improved production 16,000 tons/month is now more than 20,000 tons Accurate temperatures with minimized variance AFTER Proficy drives consistency and predictability to increase throughput 28
Slag plant milling circuit increases quality CHALLENGES Pulp level instabilities in the flotation plant Instability in the flotation, variable grind, flow rate Density fluctuations in the milling circuit OPERATIONAL RESULTS Reduced variation on feed rate from 30% to 5% Reduced sump density standard deviation from 8% to 4% Reduced cyclone feed pressure standard deviation from 90% to 45% Proficy improves plant stability and process performance. 29
Industrial Performance & Reliability Center Right Information, Right Person, Right Time Early Identification of Emerging Issue Proficy identifies an emerging issue Confirmation and Detailed Diagnosis Customer Reliability Engineer and Manager confirm issue and provide additional diagnosis Case Creation SmartTracking is a shared case management system Issues are Reviewed with Client Most issue are reviewed in a weekly report and call High priority notifications for fast moving issues Customer Feedback and Value Tracking Operational and Maintenance information is use to maintain models Actionable notifications are tracked on a quarterly and yearly basis 30
How it Works. Real time analogue data through plant control network Real Time data through Secure VPN Equipment control systems Onsite Data Collector Industrial PRC Weekly performance reports & reviews Field service engineers Operators Engineers Managers Trending; Advisories; Case Management Email notifications; Customer calls 31
Maximize mining and operations Increase Efficiency & Availability Reduce Cost and Surprises
Maximize mining and operations INCREASED Productivity & Availability REDUCED Costs & Surprises Stabilize the Process Optimize the Process for Break-through Performance Optimize Control Strategies; Use Model Based Multivariate Control Prevent Failure and Maintain Optimal Equipment Performance Stabilize Base Layer Control: Continuous Monitoring & Regular Tuning Use Predictive Analytics to Monitor Performance and Prevent Surprises 33