“predictive maintenance in a white - boussias conferences · 17/10/2019 · predictive...

Whirlpool Corporation - Confidential

“Predictive Maintenance in a White Good industry: current status,

myths and facts”Pierluigi Petrali – Manufacturing R&D Manager


A LEADERSHIP POSITION

WHIRLPOOL CORPORATION

72 million products sold in more than 170 countries in the world

World’s leadingmajor home appliance company (NYSE:WHR)

Approximately

$21 billion in sales in 2018

92 000 employees

70 manufacturing and R&D centers

$1 billion investment in capital and R&D centers annually


OUR GLOBAL PORTFOLIO

*Whirlpool ownership of the Hotpoint brand in EMEA and Asia Pacific regions in not affiliated with the Hotpoint brand in the Americas

*


EMEAMelanoItaly

Comunanza Italy

CassinettaItaly

SienaItaly

NaplesItaly

IsithebeSouth Africa

YateUK

WroclawPoland

ŁódźPoland

RadomskoPoland

Lipetsk Russia

Poprad Slovakia

ManisaTurkey

Carinaro Italy

INDUSTRIAL FOOTPRINT


EMEA

BRANDS AND PRODUCT LEADERSHIP

Indesit Built-in Suite

Indesit Innex

Whirlpool SmartCook

Whirlpool SupremeCare

KitchenAid Black Steel Collection

KitchenAid Culinary Center

Whirlpool Corporation - ConfidentialWhirlpool Corporation - ConfidentialWhirlpool Corporation - Confidential

Maintenance in the perspective of World Class Manufacturing


Whirlpool Production System TECHNICAL PILLARS

7


Vision

➢ Obtain and maintain high machine efficiency by▪ Eliminating breakdowns▪ Reducing component replacement frequency / costs

Needs

➢ To maintain efficient, breakdown free machines➢ To continuously improve machine functionality

Objectives

➢ Zero Breakdowns➢ Increase the technical availability➢ Reduction of maintenance costs

INTRODUCTION TO PROFESSIONAL MAINTENANCE

8


7 STEPS OF PM PILLAR

STEP 2

STEP 3

STEP 4

STEP 5

STEP 6

STEP 7

STEP 1

STEP 0

Preliminaryactivities

REACTIVE PREVENTIVE PROACTIVE

Elimination and preventionof accelerated deterioration

Reverse deterioration (Breakdown Analysis)

Establishment of maintenance

standards

Countermeasures against weak points of

the machine and lengthened equipment

life

Build a predictive maintenance system (trend management)

(CBM)

Maintenance Cost Management

Establishment of a planned maintenance

system

Build a periodic maintenance system

(TBM)

9


Ma

inte

nan

ce M

ix

Key Machines (AA & A Class)Other Machines (B & C Class)

Autonomous Maintenance (Cleaning, Inspection, Lubrication, Refastening Activities)

Breakdown Maintenance

Time Based Maintenance

Corrective Maintenance

Condition Based Maintenance

TYPE OF MAINTENANCE AND TYPE OF MACHINES

10


Whirlpool approach to Industry 4.0


I4.0 TECHNOLOGIES MAJOR APPLICATIONS

CoreTechnological

Areas

Use Cases● Scouting: 81● Trial: 8● Pilot: 29● Deployment: 2

Augmented Reality

Human Centered

Automation

Data Visibility

Advanced Testing

Traceability

Training on critical workstations

Augmented Reality

Training on critical workstations

Virtual Reality

3D digitization for full line and equipment

Collaborative Automation

Cobots for panel screwing

Collaborative Automation

Mobile robots for material delivery

Visibility and Analytics

Cloud based KPI’s

Visibility and Analytics

Real Time Energy Management

Vision Systems

Smart Quality Control

Industrial Inspection

Drones for visual and thermal inspection

Real Time Location Systems

Forklifts and tuggers tracking

Tools at Suppliers Location

E Tooling. Remote performance monitoring

1

http://drive.google.com/file/d/1bZ6sn2-un5tkUoCcFgoGlKtTVRaqHqi2/view


EU cooperation as Innovation Engine for I4.0

EU Research Projects

EIT Manufacturing

I4.0 CORE AREAS IMPLEMENTATION

Supplier

Scouting

● Contact/

evaluate/ engage

suppliers

● Identify/ develop

solutions

Trials

● Technologies try

out in the factory

○ Assess

technology

solution

applicability

○ Address

process/

organization

impact

Pilot

● First complete

implementation in

a factory narrow

context

○ costs/ benefits

identification

○ process/

organization

harmonization

Standardization

● After one/ two

completed pilot

extensions

● Use case ready

to be replicated

(TO BE):

○ Standardized

capabilities

○ Technical/

systems/

process/

organization

requirements

Deployment

● Gap analysis for

implementation

identified in each

factory:

○ Technical

○ Organizational

○ Opertional

● Handover

completed and

Local/ central

competence

available

● STD refinement

Rollout

● STD Solution

ready for roll

out

● Full roll out

costs known

● Prioritization

and

implementatio

n driven by

factories

Technology

Scouting

● Identify solutions

according to

knowledge intake

process

○ R&D

○ Competence

centers

○ Benchmarking

○ Trade shows

FactoryOE - Tech

Technical Readiness

Key Deliverables

First Pilot complete

Communication Events

OE - I4.0Main Accountabilities

Functional Readiness Cost/ Organization Readiness

1

21

Concepts

Core Areas

Technologies


Research projects on Predictive Maintenance:

General Information

06.11.18 16

DE

FR

I

E

I

TG

R

C

Y

▪ Topic FOF-09-2017

Novel design and predictive maintenance technologies for increased operating life of product system

▪ Duration 36 Months

▪ Start 01.09.2017

▪ End 31.08.2020

▪ Total costs € 6,248,367.50

▪ Max grant € 4,847,836.25

Whirlpool Plant under experimentation

• Clothes dryer drum production line

• Installed in Q1 2018 in Lodz factory in Poland

17.10.2019 17

Feeding Line StraightenerHydraulic Punching

Automatic Buffer

Seaming Flanging Spokes

AssemblyDrum Pre-Assembly

Flange Seaming

Dimensional Control

Unloading

UPTIME Deployment

17.10.2019 18

TO-BE Business Process

17.10.2019 19

Da

tafl

ow

Predictive Maintenance Order

Generation

Order Validation from

Maintenance

Supervisor

FMECA & Physical Measures To Be Monitored

20

• One objective of the FMECA is to address the monitoring of physical measures by means of sensors that could contribute to failure modes/causes prognosis and failure rate estimation

• Some parameters are already monitored by the PLC of the Substations – i.e. Part Gauge

• New physical measures have been identified and new sensors has been installed in March 2019 – i.e Equipment Gauge

Part Gauge Data preliminary analysis

17.10.2019 21Pierluigi PETRALI

• Anomaly detection

Tim

e-s

eri

es

Pre

dic

tio

n

WP5 Lessons Learnt (so far!)

17.10.2019 22

Need for high quality dataset

High quantity…

crucial

Collaboration

between Process

Experts and Data

Experts

2019-10-17 23

List of participants

Participant No * Participant organisation name Country

1 (Coordinator) COMAU S.p.A. IT

2 Finn-Power Oyj FI

3 VDL Weweler BV NL

4 WHIRLPOOL EMEA SpA IT

5 Kone Industrial Ltd FI

6 Engineering Ingegneria Informatica S.p.A. IT

7 OCULAVIS GmbH DE

8 SynArea Consultants S.r.l. IT

9 DELL EMC IE

10 Laboratory for Manufacturing Systems & Automation GR

11Fraunhofer Gesellschaft zur Förderung der angewandten

ForschungDE

12 VTT Technical Research Centre of Finland Ltd FI

13 TRIMEK S.A. ES

14 Politecnico Di Torino IT

▪ 14 partners, 7 EU member states, across Europe

WP5

2019-10-17 25

Overall Concept

Smart data collection

module

Analog Ethernet

USB

Data 4.0 Box

5G

Edge-computing

Condition monitoring

Data

management

AI-

machine

learning

Physics-

based

Data-driven

AR based tools for remote

assistance

Maintenance

personnel support

WP2

Remote control of

factoryData communications

(e.g. OPC-UA)

HW & SW integration

Communication

interfaces (e.g. 5G

interface)

Data analytics in cloud computing

(near real time application)

Plug-and-play cloud-based platform

AI CbM, Data analytics

Hybrid modelling

Planning of maintenance

WP3 WP4

Industrial needs, requirements, pilot cases definitionWP1

Demonstration activities

White goods industry

Metrology industry

Elevators production

Steel parts production

WP6

OCULAVIS

Finn-Power

VTTIPT

ENG

KONE

Robotics industry

2019-10-17 26

Demonstrator Description

Pressure sensors

Temperature sensors

Flowmeter

Pressure sensors

Temperature sensors

Flowmeter

Factory SQL Database

2019-10-17 27

WHR Testbed

Factory SQL Database

Pressure sensors

Temperature sensors

SCADA

SERENA Gateway

Linux Docker

- Breakdown- Repair Time

Future dataMechanical Sensors

SAP PM data

REST

Flowmeter

Alarms

2019-10-17 28

▪ Dataset available since April 2018

• Components flow, temperature, pressure

measured at Mixing Head

• Machine alarms

• More than 65000 cycles available

• SAP/PM activity

▪ Preliminary Analytics on data

▪ Testbed implementation

Work Progress

2019-10-17 29

▪ Data Analytics outcome:

• Analytics Methodology prepared with POLITO

• Mixing Head diagnosis PRETTY DIFFICULT with only pressure, temperatures and mass flow data

• Current data could more likely provide diagnosis on other components (Pumps, Valves, etc.)

▪ Change management

• Roles and skills gap has been identified

▪ OT: process engineers need for a more data driven knowledge of the physics behind

▪ IT: Data Architects need for a basic knowledge about Deep Learning Analytics

▪ Data Scientist role is missing.

Highlight of Significant Results

SERENA Intermediate Review Meeting │Turin │ Italy

Predictive Maintenance learning and challenges

● Identify the right data to be used using FMECA or other analytical tool● Act in advance: machine learning can require many months (years?) of data● Define a standard, semantic data model● Have an IT architecture to ease up data flow from sensor to storage to

applications● Provide mechanism to correlate different kind of data● Find a way in which process expert and data scientist can effectively

exchange information and knowledge● Prediction must gain expert trust● Enable the organization and business process to interact and work with

PdM


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31

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“predictive maintenance in a white - boussias conferences · 17/10/2019 · predictive...

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