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© 2019 AVEVA Group plc and its subsidiaries. All rights reserved.
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Presented by:
Session ID: AP-03
Evolving from Predictive to Prescriptive Analytics
© 2019 AVEVA Group plc and its subsidiaries. All rights reserved.
Joris Verlinden, APM Portfolio Strategy Manager
Christian-Marc Pouyez, Director APM Advanced Analytics
Mike Reed, Manager - Engineering & Analytics
13-November 2019
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Save $100s of Millions – Reduce Downtime with Predictive and Prescriptive Analytics
Asset
Condition
Failure
Prediction
Operate & Maintain
Business Risk
Prescribed action
Risk MitigationAsset Strategy
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Asset Performance Management 4.0Connecting Person to Asset to Optimize Asset Condition & Performance and Warrant Asset Compliance
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RoadmapAsset Strategy Differentiation & Optimization
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Asset Breakdown Structure (ABS)Functional Decomposition of Asset by Process Steps and into Independent Systems
Goal & Benefit• Partition Facility into logical Asset Tree• Enables Fast Asset Criticality Ranking• Equipment inherits Meta Data that is defined at
desired level in Asset Tree (e.g. Maintainability, Deferment cost)
System Definition• Clear Function Definition• Independence (Risk/Criticality)• Isolation is Safe & Technically Feasible• Cluster Tasks (Work Order Scope)• Cost Reporting possible at each level in Asset Tree• Define each Sub-System as large as possible to
create efficiencies (Minimize Number of Systems)
Facility
Plant
Unit
System
Equipment
Maintainable Item
Failure Mode
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Equipment Breakdown Structure (EBS)Standard Functional Decomposition of Equipment (ISO 14224)
Equipment
Maintainable Item
Failure Mode
Goal & Benefit• Group Equivalent Equipment Types into
Classes• Standardization, Consistency & Optimization• Fast EAM deployment
Asset Strategy Library – Part I• Equipment – Maintainable Item – Failure Mode• Maintainable Item (MI) is the smallest component/
part that is repaired or replaced in the Equipment• Failure Mode (FM) is one or more ways in which
each MI can fail• Standard way to create EBS is ISO 14224• Define Mean Time to Fail (MTTF) and Mean Time to
Repair (MTTR) per FM.• Define Failure Distribution (Weibull) per FM• Fixed vs Context data (Based on meta data: e.g.
Product Medium, MI material, Temperature, Load)• Equipment and MI Value & Age (Life Cycle Cost
Analysis – LCCA)
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Risk Based Maintenance - RBMFunction, Failure Mode & Failure Effect
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Asset Strategy OptimizationPreventive vs Corrective Strategies that mitigate Failures to an acceptable level
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APM Predictive AnalyticsCondition Monitoring, Anomaly Detection & Failure Mode Prediction
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Predictive APM = Event Based Asset StrategiesPredictive APM requires “Pit-Stop” approach to Execute Tasks within P-F Interval of the Alert
Predictive Asset Strategies trigger unforeseen EVENT BASED Work Orders.
Time & Usage based Strategies (“Classic”) schedule anticipated PLAN BASED Work Orders.
Event Based Tasks require Pit-Stop Approach for successful execution• Demands careful Preparation• Requires Agility and Discipline• High level of Execution Maturity in Processes &
Organization• Requires advanced Tools for Prioritization, Scheduling
& Tracking of Work Orders: Operationalize, EAM, Mobile, Control of Work, MES, Skelta)
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For Internal Use Only
Case: Centrifugal Pump in Oil & Gas Industry
Real Life Example, Middle-East
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Business CaseRealize Value with APM Prediction and Prescription
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Objective
• Reduce the scope, workload and duration of Turnarounds.
Method
• Use Machine Learning (PRISM) to monitor sensor data, diagnose equipment
anomalies and predict failures. Postpone Tasks to next Turnaround.
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Before: Sensor-Centric Approach
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Model & Fault Diagnosis
• Group similar types of sensors into
one Model (e.g. not combine TEMP
and VIBR)
Anomaly Detection
• Only detection of Anomaly, no Failure
Prediction with Actions
Fault DiagnosisModel
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After: Failure Mode Centric Approach
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Fault Diagnosis:Rebuilt with Failure
Mode Focus
Model:Rebuilt with Failure
Mode Focus
ISO 14224:Maintainable Item
PML:Failure Mode
Prescriptive Actions
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Pump Centrifugal (PUCE): P&ID
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Pump Centrifugal (PUCE): Metrics/Sensors
Mechanical Model
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Process Model
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Pump Centrifugal (PUCE): ISO 14224
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Pump Centrifugal (PUCE): ISO 14224
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Pump Centrifugal (PUCE): Failure Modes (Diagnostics)
Mechanical Model
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Process Model
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Pump Centrifugal (PUCE): Prescriptive Actions
Mechanical Model
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Process Model
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Templates: Failure Mode Prediction + Prescriptive ActionsSome examples available today
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Compressor (CO)
• Centrifugal (COCE), Screw (COSC), Reciprocal (CORE)
Gas Turbine (GT)
• Aero-Derivative (GTAD), Industrial (GTIN)
Pump (PU)
• Centrifugal (PUCE)
Electric Motor (EM)
• Alternating (EMAC)
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linkedin.com/company/aveva
@avevagroup
ABOUT AVEVA
AVEVA is a global leader in engineering and industrial software driving digital transformation across the entire asset and operational l ife cycle of capital -intensive industries.
The company’s engineering, planning and operations, asset performance, and monitoring and control solutions deliver proven results to over 16,000 customers across the globe. Its customers are supported by the largest industrial software ecosystem, including 4,200 partners and 5,700 certified developers. AVEVA is headquartered in Cambridge, UK, with over 4,400 employees at 80 locations in over 40 countries.
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© 2019 AVEVA Group plc and its subsidiaries. All rights reserved.