Big data: From theory to practice - a maintenance ......Big data: From theory to practice - a...

Big data: From theory to practice - a maintenance application on wind turbines

High Tech Meets Data ScienceFebruary 9, 2017

Alessandro Di Bucchianico

Joint work with Stella Kapodistriaand Thomas Kenbeek

2 Big data: From theory to practice

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Thomas Kenbeek Winner SLF Award 2016

Big data: From theory to practice

http://www.servicelogisticsforum.nl/nl/nieuws/29/9e-slf-afstudeeprijs-voor-bachelor-scriptie

http://www.servicelogisticsforum.nl/nl/nieuws/29/9e-slf-afstudeeprijs-voor-bachelor-scriptie

• Over 30 research groups from six different departments of TU/e– Very diverse mix of scientists, all interested in working with and

on real world data• Coordination & cooperation in research programs

– Customer journey– Health analytics– Internet of things– Quantified self– Smart manufacturing and maintenance

• Virtual center, with small own staff (2.2 FTE)– Scientific Director: Wil van de Aalst– Operational Director: Mark Mietus– Program Manager: Joos Buijs– Secretary / communication: Henriette de Haas / Patricia Knubben

Data Science Center Eindhoven

www.tue.nl/dsce


http://www.tue.nl/dsce

• Window to the world: Inside-Out & Outside-In – By operating together we are more recognizable and easier

accessible both to companies as well as to (semi) governments (entry point, website, etc.)

– To influence the (inter)national agenda and to initiate larger initiatives (flagships, etc.) we need to collaborate.

– Representation in various DS bodies (DSPN, BDA, C2D, BVDA, etc.)

• Meeting place, connecting people, exchanging ideas– Meeting people from other disciplines and organizations (lectures,

summits, news, …)

DSC/e mission


Google images


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Industrial setting


4OFFSHORE


Industrial setting

3G

HQ IJSSEL TECHNOLOGIES

SERVER

WEB SERVER


Industrial setting

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Expected impact

Timely detection of failures Detection of complex failures (several causes / multiple failure modes) Empowerment van asset owner

Goal:Algorithmic approach to condition based maintenance

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Project Goals and Impact


Condition Based MaintenanceModel elements• deteriorating/degradation mechanisms• failure types• monitoring schemes (for condition of device/system)• maintenance actions

What is the objective?• optimal maintenance time• proper maintenance action (minimal, corrective,. . . )• optimal inspection scheme (when to inspect, how many samples to

collect?)



Problem statement: detailsData:• Component condition

– Temperature– Vibration

• Speed• Pitch angle• Yaw• Operating state• Power output

• Environmental conditions

• Event & maintenance logs

Data from every turbine :3GB per day*365 days=1.095TB per year

Objective:Monitoring, prognostics and diagnostics of wind turbines


Step 1: Identify baseline period (19 June 2013 – 18 Oct 2013)

Step 2: Only look at main generator operating & connected

Step 3: Build an algorithm considering all available relevant data

Step 4: Verification

From data to maintenance

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Model Single component

(perfect, good, failed)

Monitoring instances

Noise ~N(0,𝜎𝜎2)

Time

Cond

ition

State of the component

Perfect

Good

Failed

Objective: identify in a timely fashion the change of the condition

Approach:

Consider a baseline (perfect state)

Derive upper and lower alarm limits (not specification limits!)

The approach resembles multidimensional Statistical Process Control (SPC)


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3G

HQ IJSSEL TECHNOLOGIES

SERVERPARKOWNER

WEB SERVER


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Current Approach – Static Limits


Regression Approach


Bearing monitoring: temperature

Big data: From theory to practice24

Step 1: Identify in-control period (19 June 2013 – 18 Oct 2013)

Step 2: Only look at main generator (working)

Step 3: Build algorithm

Bearing monitoring: temperature


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Model Warnings for imminent failure

Nacelle temperature Oct-Nov 2013

Oil temperature Oct-Nov 2013

Bearing temperature Oct-Nov 2013

Gearbox temperature Oct-Nov 2013

Main gen. temperature Oct-Nov 2013

Starting gen. temperature Oct-Nov 2013

Power output main gen. Oct-Nov 2013

Power output starting gen. None

Prediction of Dec 2014 failure


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Results

Time

Cond

ition

State of the component


1. Automated alarms based on dynamic joint thresholds for– Bearing– Gearbox– Generator

2. Overall health index 3. Power output prediction

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Practical Issues• sanity checks on data

– coding of missing data (0, 999,…)– variables with trivial behaviour– different names for variables for different customers– recalibration of sensors– unspecified time zone + summer/winter time

• pre-processing of data– binning and averaging of data (kills system dynamics)– undocumented approach to vibration data

• ownership of data (OEM versus park owner)• different settings after maintenance (e.g., RPM generator)• ...



5Vs of big data

5Vs of big

data

Volume

Velocity

VarietyValue

Veracity

Ron Kenett and Galit Shmueli

Increase InfoQ at the Design stage Post-data collection stage

by assessing the InfoQ - Eight Dimensions Data resolution Data structure Data integration Temporal relevance Chronology of data and goal Generalizability Construct operationalization Communication


Veracity, Information quality — InfoQQuality is evaluated in terms of the usefulness of the statistics for a particular goal.

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Further refinements• involve layout of wind park (position of wind turbine)• involve control policy of wind park• go from warnings to alarms• EWMA / CUSUM charts• .....


Key Insights

1. Use dynamic thresholds based on joint use of measured quantities:– reduction of false alarms– timely detection of complex failures

2. Combine techniques of CBM and SPC :– data collection from CBM may be beneficial for SPC activities and

vice-versa– detection of out-of-control situations may avoid situations that

have increased failure rates


Big data: From theory to practice - a maintenance ......Big data: From theory to practice - a...

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