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transcript

Presented by:

Session ID: AP-03

Evolving from Predictive to Prescriptive Analytics

Joris Verlinden, APM Portfolio Strategy Manager

Christian-Marc Pouyez, Director APM Advanced Analytics

Mike Reed, Manager - Engineering & Analytics

13-November 2019

Save $100s of Millions – Reduce Downtime with Predictive and Prescriptive Analytics

Condition

Failure

Prediction

Operate & Maintain

Business Risk

Prescribed action

Risk MitigationAsset Strategy

Asset Performance Management 4.0Connecting Person to Asset to Optimize Asset Condition & Performance and Warrant Asset Compliance

RoadmapAsset Strategy Differentiation & Optimization

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

System

Equipment

Maintainable Item

Failure Mode

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)

Risk Based Maintenance - RBMFunction, Failure Mode & Failure Effect

Asset Strategy OptimizationPreventive vs Corrective Strategies that mitigate Failures to an acceptable level

APM Predictive AnalyticsCondition Monitoring, Anomaly Detection & Failure Mode Prediction

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)

For Internal Use Only

Case: Centrifugal Pump in Oil & Gas Industry

Real Life Example, Middle-East

Business CaseRealize Value with APM Prediction and Prescription

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.

Before: Sensor-Centric Approach

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

After: Failure Mode Centric Approach

Fault Diagnosis:Rebuilt with Failure

Mode Focus

Model:Rebuilt with Failure

Mode Focus

ISO 14224:Maintainable Item

PML:Failure Mode

Prescriptive Actions

Pump Centrifugal (PUCE): P&ID

Pump Centrifugal (PUCE): Metrics/Sensors

Mechanical Model

Process Model

Pump Centrifugal (PUCE): ISO 14224

Pump Centrifugal (PUCE): Failure Modes (Diagnostics)

Mechanical Model

Process Model

Pump Centrifugal (PUCE): Prescriptive Actions

Mechanical Model

Process Model

Templates: Failure Mode Prediction + Prescriptive ActionsSome examples available today

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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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 … AWC NA Presentations Day...Save $100s of...

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