performance for assets · Ø recurrent savings of € 500.000 / year on energy equivalent to 7.000...

PerformanceforAssetsTurningdataintoactionableintelligence

www.performanceforassets.com

Half of my expert-personnel will retire in the near future

Can processes be optimized

and how?

How do I ensure safe and reliable operation of aging equipment?

Where can I save energy?

Why does my machine not perform as

expected?

How can meantime between failures be

extended?

How to enhance the value of existing

products & services?

Today’s Industrial Challenges

Productionprocessescontinuouslygenerate

massiveamountsofdata

but...

“Onaverage,between

60%and73%ofalldatawithinanenterprise

goesunusedforanalytics”-Forrester

Moreover,traditionalconditionmonitoringsystemsgiveanarrowviewonprocessbehaviour


Tobroadentheview,wecorrelatetheconditionmonitoringmeasurements

withtheoperationaldataandcentralize,filterandsynchronizeallavailableinformation

PLCCMS

Furthermoreweevaluateandintegrate

theotherunstructureddatasourcesthatcanpotentiallyberelevant


Detectanomaly

Diagnose:Wheredoestheanomalycomefrom?

Prognose:Howdoesanomalyevolveandwhentotakeaction?

Actionable intelligence

for performance optimization & predictive maintenance

Our approach: Methodology

Immediate/onlineactionsSafety,reliability&continuity

Mid-termactionsPlanning,savings&performance

LongtermactionsProcedures,strategy&investments

Our approach: Actionable Intelligence

Combiningprocess/assetknowledgewithdataanalytics..

..isthekeytosuccess!

Machine learning: 3 approaches

Onlineconditionmonitoring

Onlineidentificationofanomaly

Offlinehumandiagnosis

Physicalmodels

Theoreticaloptimumcondition

Impossibletoincludeallhypothesis

Datadrivenmodels

Historicalprocessdata

Relativeviewonequipmentstatus

!

Datadrivenmodels

Our approach: Hybrid model

Onlineconditionmonitoring

Physicalmodels

Hybridmodels

Robustmodelsandoptimizedmachine-learning

bycentralizing,synchronizingandanalysingallassetdata!

Weuse+30provenanalytictoolssuchasthedecisiontreeto:

Rule 2

Rule 4 Rule 3

Rule 1

Our approach: Analytic tools

1.  Analysethehistoricaldata

2.  Automaticallyselectthekeyparametersthatrelatedtoadrift/deviation

3.  Createrulesbycombiningdifferentparameters

4.  Testtherobustness

Aftervalidation,therulescaneasilybecopiedtoallsame type equipment and are automaticallyadapted (auto-learning) to the different operatingconditionsofeachuniqueequipment

PREDICTIVE MAINTENANCE

PERFORMANCE OPTIMIZATION

COST REDUCTION & REVENUE INCREASE

€

€

Our goal: Providing actionable intelligence for…

ENERGY SAVINGS COMPENSATION OF LOSS OF KNOWLEDGE

2008 R&D Project

2009 Various industrial

pilot projects

2011 Implementation

on 52 wind turbines

2016 Application on steam turbines

and CNC machines

2017 Establishment of

P4A

Ø  Startedin2008asanR&Dprojectfocusingonthedevelopmentofaremoteassetmanagementplatformforrotatingequipmentinindustry–needofintelligentmonitoringtoolforourO&Mproductionbasedcontractsintheconventionalindustry

Ø  Healthmonitoringplatform,modelingbasedusingthelatestofdataminingtools

Ø  Since2008~6million€wasinvestedtodeveloprobust,reliablemonitoringandmanagementtoolforrotatingequipment

Ø  In2017,PerformanceforAssetswasestablishedtomarketthiscollaborativeplatformtoalargeraudience

Track Record

Projects

Case1:Improvedproductionuptime&safety

Howtoavoidreturningfailures

onacriticalmixerinachemicalplant

causinglongdowntimeandsafetyrisks?

Case 1: Improved production uptime & safety


Beforerepair

Afterrepair

Lookingatthedatawithtraditionalchartsandplots,itisnoteasytoseeifpatternshaveoccurredbeforethefailure

Thehealthindicatorchangesbehaviourassoonasthemixerentersinabnormalconditions(4monthsbeforethefailure!)

Failure

Normaloperationbeforethestartofthedegradation

Machinelearninghelpstoidentifywhichconditionsondatadefineahealthysituationoranupcomingfailure

Failure


Step1:DetectionofanomalyØ Useofhistoricaldatatoanalyseconditionsbefore/afterafailureandidentifykeyfactorsto

establishahealthindicator

Ø Unhealthyconditionscanbedetected4monthsbeforethefailure

Step2:DiagnosisØ  Rootcauseanalysisbasedondataandournewhealthindicator

Step3:PrognosisØ  Predictionofremaininglifetimebasedonnewhealthindicator

Step4:Intelligence-PredictiveMaintenanceØ  Implementationofapredictivemaintenancetoolwithalarmthresholdstoprotectthe

equipmentfromupcomingfailures

Case 1: Improved production uptime & safety

Thebenefits

Ø  Improvedproductionreliabilityandsafetythroughpredictivemaintenance

Ø  Automatedalarmstoprotectequipment

Ø  Easyimplementation

Ø  Self-learningbigdatamodel

Case2:€500.000/yearenergysavings

Howtooptimizethesteamextraction

ofthesteamnetworkinourphosphateplant

andtherebyminimisetheenergybill?


Operatordashboardforoptimizedsteamextraction

Extractfromthedecisiontree(model)createdbyP4Atoindicatethebestwaytooperatethesteamnetwork

Step1:DetectionofinefficienciesØ  A+100.000tonsofsteam/yeargapinenergyefficiencywasrevealedthroughourprocess

analysisanddataminingapproach

Step2:DiagnosisØ  Rootcauseanalysisthroughvariabilityexplorationofhistoricaldataandprocessthankstoour

analyticstool

Step3:PrognosisØ Wasteofsteamextractionof5tons/hour,resultingina€60/hourfinancialloss

Step4:Intelligence–PerformanceOptimizationØ  Installdashboardtomonitorextractionflow(actualvsoptimum),improvecommunication

betweenvariousdepartmentsandenhancereportingpractices


Thebenefits

Ø  Increasedsteamextractionof5tons/hour

Ø  SignificantcostreductionØ  Bettermanagementandmonitoring

Ø Moresustainableoperations

Ø  Implementedin<3months

TheROI

Ø  Recurrentsavingsof€500.000/yearonenergyequivalentto7.000tonsofCO2peryearingasconsumption,ora15%reduction


Case 2: Advanced turbomachinery monitoring

Howtogetanabsoluteview

ontheconditionandbehaviour

ofmyturbomachinery?


Elaborationofamechanicaldynamicmodelbasedonreverseengineering

Case 2: Advanced turbomachinery monitoring Fine-tuningofthedynamicmodelusingthebalancingmachineInfluencesdueto;

1.  bearingdesign2.  labyrinthseals3.  impeller&turbineblades

Usingthisinputweconfigure

upto50auto-learningmodels/assetwhichwethenfollowonline!

Thebenefitsofourhybridmodel?

Ø Usingphysicalmodelsonlygivesanarrowviewontheassetbehaviour

Ø  Bycorrelatingthephysicalmodelwith

historicalandreal-timeconditiondata

wearemonitoringtheassetfromallangles

Ø Ourrobustauto-learningmodelsallow

reliable,continuousconditionmonitoring


Dashboard

Asset info with alarms

Asset health status

Various trend analysis options

ASSET MANAGER: Production analysis

ASSET MANAGER: Gap analysis

ASSET MANAGER: Power VS. Torque

MAINTENANCE MANAGER: Alarm overview

MAINTENANCE MANAGER: Failure analysis

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nbofo

ccuren

ces[-]

duratio

n[h]

Failuredistribution[FL595]

SumofOccurrences SumofDuration

MAINTENANCE MANAGER: Prognostic

Conditions Prognostic

highwind 6months

Lowwind 12months

Highwindunderpowerreduction

8month

Services

Data value chain

Dataacquisition Datastream Storage Analytics Visualization

Ø  PLC/DCSØ  CMSØ  ImagesØ  TextØ  VideosØ  Sound

Ø  QualityØ  RelevancyØ  Cyber

securityØ  GovernanceØ  Gemalto

Ø  OnpremiseØ  PrivatecloudØ  Bigdata

Ø  P4AModelsØ  DataMaestro

(Analytics)Ø  OpenSourceØ  PhysicalØ  Clustering

Ø  DashboardsØ  TrendingØ  ChatbotØ  Opensource

IBM

Wintell

Servicepackages

Approach:Processandbusinessunderstanding1.  Identifyneedsandconcerns

2.  Identifydata/informationalreadyavailable

3.  DefineKPI’s

Targets:Presentpotentialimprovementsandassociatedsavings1.  Offlineanalysisofhistoricaldataandreporting

2.  Onlineimplementation

3.  Additionalquickwinsmodelsusingonthesamedataintodifferentareas

Howtogetstarted?

FLASHANALYSIS

Deliverable:Reportwithopportunitiesforprocessoptimization/PDM

Project flow

Step 1: Process & Business Understanding Ø  Interview with process engineers to define main Kpi(s) Ø  Evaluation of P&IDs / PFDs Ø  Evaluation of data availability / Key data identification

Step 2: Flash Analysis Ø Definition of key performance indicators (KPIs) Ø  Performance & maintenance benchmarking Ø  Assessment of potential improvements opportunities (cost savings, performance,

maintenance) Ø  Evaluation of constraints or road blocks

Step 3: Workshops Ø  Workshops with operators and plant staff for Ø  Brainstorming root causes of variability and process improvement ideas Ø  Objective to engage operators in the project Ø  Ideas presented on a root cause tree

Step 4: Advanced Analytics Ø  Preparation of historical data -> KPI calculation (flag possible data quality issues) Ø  Analyse trends and assess long, mid-term (like seasonality) and “local” variability Ø  Compare performance, failure rates, with best historical performance or industry

benchmarks Ø  Define a realistic target considering production, availability and quality requirements Ø  Quantify the performance gap and failure rates reduction Ø  Estimate the potential cost savings

Project flow

Step 5: Model development & offline testing Ø  Key parameter identification Ø  Model development and evaluation Ø  Model testing offline with independent/new data sets

Step 6: Online implementation

Ø  Setup of prototype dashboard through Wintell web portal Ø  Online implementation

Project flow

Do your assets need a performance boost? www.performanceforassets.com

performance for assets · Ø recurrent savings of € 500.000 / year on energy equivalent to 7.000...

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