THE DIGITAL AND PERSONNEL CHALLENGES · 2019-01-31 · THE DIGITAL AND PERSONNEL CHALLENGES OF...

THE DIGITAL AND PERSONNEL CHALLENGESOF MODELLING DATA FROM MORE THAN 15,000 WTGS

Operations and Maintenance Summit 2019Jan 29th 2019 | TORONTO, ONT, CANADA

NEM Solutions Proprietary and Confidential

Gorka ParadaCEO NEM Solutions USA Inc.

THE STORY OF A JOURNEY:genesis

MOBILITY

ENERGY

THE STORY OF A JOURNEY:current location

✓ Self-funded from the beginning.

✓ Profitable from Year 1.

✓ Never needed VC.

Say hello to a predictive world

WE TURN DATA INTO STEP AHEAD SOLUTIONS

EXCELLENCE IN PREDICTIVE ANALYTICSYOUR SUCCESS IS OUR GOAL

A.U.R.A. IS ALL AROUND THE WORLD

+10OEMs

GAIN SAVINGS

IMPROVE

AVAILABILITY AND

PRODUCTIVITY

ASSET

MANUFACTURER

COMPATIBILITY

ASSET LIFE

EXTENSION

EARLY FAILURES

DETECTION

KNOWLEDGE

GENERATION

BENEFITS

MULTI-OEM

MULTI-PLATFORM

REAL TIME CONDITION MONITORING

VARIABLES CENTRALIZED AND

HOMOGENIZED

ADVANCED DIAGNOSTIC

CAPABILITIES

USER FRIENDLY

FEATURES

MULTI-USER ACCESS

USER- DRIVEN MODULES

CLOUD SOLUTION

BIG DATA ECOSYSTEM

HIGH AVAILABILITY SERVER

INTEGRATION CAPABILITIES

FEATURES

THE STORY OF A JOURNEY:the ground already walked

BE AWARE

CHANGE

PROCESSES

People must take ownership of the

digital transformation and embrace change so that it becomes a

part of the new normal.

ASSET DATA ACQUISITION

SCALABILITY OF THE PLATFORM

DIAGNOSTICS CAPABILITY

DIAGNOSTIC FOLLOW UP

VALUE CHAIN

PROJECT MANAGEMENT

DATA: GENERATION AND INTEGRATION INTO A DIAGNOSTICS PLATFORM


DIAGNOSTICS CAPABILITY, LOGIC IMPROVEMENT AND MAINTENANCE


VALUE CHAIN

PROJECT MANAGEMENT

TURBINEMiscellaneous

• Underperformance

• Reactive power high

Sensors

• Turbine sensors out of range,

fixed, no data reception, etc.

GEARBOX (1 to 5 years)Main Bearing

• Bearing overheating

Gearbox Bearings

• Bearings overheating

Cooling

• Oil overheating

Sensors

• Gearbox sensors out of range,


H. UNIT (Up to 13 months)Cooling

• Hydraulic oil overheating

Sensors

• Hydraulic Unit sensors out of

range, fixed, no data reception,

etc.

ROTOR (Up to 7 months)Pitch

• Deviation

Sensors

• Rotor sensors out of range,


TRANSFORMER (Up to 5

months)Phases

• Phases overheating

• Phases discrepancy

Sensors

• Transformer sensors out of

range, fixed, no data reception,

etc.

NACELLE (Up to 6 months)Cooling

• Nacelle overheating

Sensors

• Nacelle sensors out of range,


CONVERTER (Up to 6 months)Grid

• Unstable frequency

Inverter

• Inverter overheating

Rectifier

• Rectifier overheating

Sensors

• Converter sensors out of range,


GENERATOR (Up to 1 year)Bearings

• DE/ NDE bearing overheating

• DE/ NDE bearings discrepancy

Phases

• Phases overheating

• Phases discrepancy

Slip ring

• Slip ring overheating

Sensors

• Generator sensors out of range,


YAW

Miscellaneous• Excesive Yaw

• Yaw Missalignement

Sensors

• Yaw sensors out of range, fixed,

no data reception, etc.

OWN INTEGRATIONS

PROTOCOL

HOMOGENEOUS FORMAT

NOMEMCLATURE

STANDARDIZATION

SOLID BASE FOR DIAGNOSTICS

AUTOMATED, QUICK AND

VERIFIED DATA INGESTION

OUR SOLUTION





VALUE CHAIN

PROJECT MANAGEMENT

INITIAL ARCHITECTURE

DASHBOARD

EXPERT

API ENGINEDDBB

TARGET

NEW ARCHITECTURE

Distributed storage

ST

RE

AM

IN

G L

AY

ER

HM

I L

AY

ER

BA

TC

H L

AY

ER

High freq. raw data

EDGE CLOUD

Low freq. statistics

Rawdata

get & storehttp

WF Metrics

Indicatorscalculation

Modelsevaluation

Indicatorsevaluation

Symptomsevaluation

HS calculation

Model trainer

Indicatorseditor

Symptomseditor

Dashboard

Conf filewith

statisticsdefinition

Data Explorer

KPI calculation

Conf filewith

metricsdefinition

Customer platform or 3rd

party APIHistorical

data

Modelseditor

datacalculations

definition





VALUE CHAIN

PROJECT MANAGEMENT

A.U.R.A. Diagnostic fault detection methodology

STEPS TO BUILD A FAULT DETECTION PROCESS:

1. Detectionof externalinfluences

2. Cleaning data

3. Normalbehaviour learning

4. Threshold selection

Hybridization

Data - drivenInterpretability

Domain - driven

Reliable early failure

detection

Failure evaluation tools

Reduce spuriousCorrelations

DATA ANALYTICS APPROACH

ANALYTICS DEDICATED TEAM

OEMs TALENT TRANSFER

CONTINUOUS IMPROVEMENT

PROCESS

LOGIC MAINTENANCE-SPECIFIC

SYMPTOMS DESIGN

PROJECT MANAGEMENT

INVOLVEMENT: A QUEST FOR

FEEDBACK.

OUR SOLUTION





VALUE CHAIN

PROJECT MANAGEMENT

Plug & Play

HEALTH STATUS REPORT

TARGET:

– Client service

– Makes the feedback follow-up easier.

CONTENT:

– Communication issues

– Sensor issues

– Failures from normality deviations list

• Detailed analysis: environmental conditions, comparison with neighbour turbines, occurrence of multiple failures in same WTG...

• Recommendations per failure

FREQUENCY: bi-weekly, monthly...

END USERS: different profiles.

– Site managers,

– Regional managers,

– Asset performance engineers...

• Agenda

– Review incidences reported.

– Review feedback from customer.

– Update project status: new symptoms deployment and symptoms improvements from feedback.

– Introduce new A.U.R.A. functionalities.

• Frequency: bi-weekly, monthly… Usually between reports

• Attendees: different profiles.

– Site managers,

– Regional managers,

– Asset performance engineers...

CONSTANT INTERACTION

OUR VEHICLE:your benefits

Company Profile❑ Top Performer European Utility ❑ + 3 GW Wind Capacity❑ Top 3 in Offshore Wind❑ Onshore heterogeneous fleet

Data Centralization❑ SCADA: 10’ Variables & Alarms❑ CMMS work orders❑ CMS data❑ Internal system HEXIS❑ Expert Performance Monitoring Team

Challenges❑ Improve failure detection.❑ Transform performance analysis issues into real savings.❑ Analytics services integration into Hexis.❑ 5 markets, 4 OEMs

Implementation❑ A.U.R.A. Diagnostic + Domain Knowledge

Outcome❑ Increased 12% failure detection relative to HEXIS❑ Improved data quality through SCADA signal sanity check.❑ Improved Performance Monitoring capabilities.Business Case

THANK YOU.

We choose to do things differently

[email protected]

THE DIGITAL AND PERSONNEL CHALLENGES · 2019-01-31 · THE DIGITAL AND PERSONNEL CHALLENGES OF...

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