Building the Learning Steel Plant · 2019-10-02 · Classical techniques are often based on...

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Building the Learning Steel PlantDr.-Ing. Markus Reifferscheid, CEO of SMS digital

Dr. Markus ReifferscheidThe Learning [Steel] Plant | Future Steel Forum 2019, Budapest

October 2, 2019


Only 71 companies remain today from the original 1955 Fortune 500 list. Digitalization is the key to competitiveness.

Who will lead the market of the future?

Digitalization in the Steel Industry

Dr. Markus Reifferscheid2The Learning [Steel] Plant | Future Steel Forum 2019, Budapest

October 2, 2019

…expect positive influences on business through digitalization

80.2 % …expect to lose competitive advantages if they don’t digitalize.92.3 %

…are investing in digitalization92.1 %

…cumulative cost reduction are expected3.2 %

…are perceived as not digitalized enough by their customers.62.6 %

… are not cooperating with partners from the same industry.83.9 %

Major drivers and need to act


What does this mean in steel industry?

„Higher, farer, quicker“• Maximum delivery performance

• Smaller order sizes

• Maximum quality

• Reducing costs „Turning data into value“

Chal

leng

esof

the

stee

lind

ustr

yGlobal Trends „New Normal“

• Global overcapacities

• Volatile prices

• Stronger regulations


October 2, 20193

Source Photo:https://www.g-geschichte.de/plus/faszination-gral/© istockphoto/estt


“We combine digitalization expertise with 150 years of metallurgical knowhow”

Who we are?• Founded in April 2016• Digitalization experts with roots in the SMS group

What we do?• Software Solution in value relevant fields• Platform solutions• Digital service and products & apps• Customer Innovation | Data analytics | Machine learning• Services for operational expertise


October 2, 20194

Successful specialists,valuablebrands

Hundreds of digitalization products, services, expertsworldwide

~ 150 years experience and expertise in metal plantengineering


Objective: Establishment of digital services in the steel industry


October 2, 20195

DigitalServices

Complex plant landscape

Harvest data Create added value

Scalability Security

Newbusiness models

New ways of working

Acquisition of resources

© All rights reserved

The Learning [Steel] Plant

6October 2, 2019The Learning [Steel] Plant | Future Steel Forum 2019, Budapest

The Learning [Steel] PlantOur Vision


Planning

Quality

Asset

Our Vision of the Learning Steel Plant

7

The Learning Steel Plantauto-adaptively optimizes its production process as part of an integrated value chain, based on physical, heuristic, and data driven based models or systems with regard to production planning, product quality and asset condition.


October 2, 2019

Logistics

Energy

SMS Data Factory


Solution categories

Dr. Markus Reifferscheid

Planning Asset

Quality

Optimization of :Plant availabilityInventory managementMaintenance planning

Products:Asset Management System &SaaS Solutions- Smart Alarm- Digital twin…

Optimization of:Output & Financials

Delivery performanceStock management

Product:MES 4.0

Optimization of :Product characteristics

TolerancesYield

Product:Quality Management

System

The Learning [Steel] Plant | Future Steel Forum 2019, BudapestOctober 2, 2019

8


Create added value – Merge domain Know-how with Machine Learning


October 2, 20199

• Need much less data

• Powerful, but hard to maintain & scale

• Capture knowledge & experience of experts

• Easy to understand

• Rigid or reliant on judgement

• Limited amount of data available

• Use parameter space of past operation

• Easier to maintain and scale

Engineering models

Equipment / process physics( physical parameter sets, etc.)

Rules & other codified insights

Accumulated experience in operating the equipment

Machine learning, pattern recognition / classification

Models

Theory-based Empirical & Heuristic Data-driven


Learning never ends!

10

Data translation Improvements & Skills

Harvest data

Domain Know-how

AI / MLData-based

learning & decision making


October 2, 2019

http://www.google.de/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwizqJn_4I3eAhWysKQKHRfMBIEQjRx6BAgBEAU&url=http://accionsocialnaval.org/give-money/&psig=AOvVaw2KR8IhPfiY9FgkALTGYpe3&ust=1539875369099803

https://www.google.de/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwjdotr0pZreAhXLsaQKHd_LBZUQjRx6BAgBEAU&url=https://pngtree.com/free-icon/value-added-earnings_769313&psig=AOvVaw08x5Fo9RPzFEQyNnNEd91T&ust=1540306201907279

https://www.google.de/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwj73u-FppreAhWsMewKHVLLCkUQjRx6BAgBEAU&url=https://thenounproject.com/term/value/680043/&psig=AOvVaw08x5Fo9RPzFEQyNnNEd91T&ust=1540306201907279


SMS Data Factory – Harness DataBrown field situation: Render the plant digital ready


SMS Data Factory


Data Factory - protects OT systems from from IT system interference- provides an organized data handling from any data source in appropriate form- and offers a central API integration layer

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API Sets

Data Factory

Conn

ecto

rsData Dictionary

Sources

<Level 0>

<Level 1>

<Level 2>

<Level 3>

<Level 4>

<Apps>

<...>

Data Types

Relational Data

Files/Media

Time Series

Data Engine

Data Warehouse

Data Lake

Acquisition Module

OT/IT Systems

AI / ML

Web Apps

UniversalAPI

CustomAPI

CloudOn Premise



Advantages of a Cloud Platform Solutions

Dr. Markus Reifferscheid13

Focus on core business Quick implementation

Robust securitystandards & encryption

Worldwide availability and connectivity

Endless scalability Future proof technology



mySMS Platform | Connectivity to SMS Platform ServicesFocus on your core business

• Make use of specialists know-how regarding security, infrastructure

• Only pay for what you use

• Higher flexibility and scalability

• Commonly shared components enable a faster time to market and cut development costs

14

• Installing an equivalent on-premise solution is • Costly• Time-consuming • Requires specialized staff

• Cloud computing is no core competence to companies in the steel making industry• Velocity in technology requires

specialized companies that are able to keep up

• Lack of knowledge in terms of data security, infrastructure etc.

• Scalability: Changes in business will cause costly changes in IT infrastructure


October 2, 2019

© All rights reserved Dr. Markus Reifferscheid

15October 2, 2019The Learning [Steel] Plant | Future Steel Forum 2019, Budapest

Asset SolutionsIMMS | Genius CM | PCA | Smart Alarm | eDoc


Digital Products and Apps


• Increases OEE by avoiding equipment downtimes• Works as asset management system connected to

embedded sensors• Shows plant status by online alarm signal indication • Supports with root cause analysis including process

data• Embedded Intelligent Sensor Systems: Torques,

Vibrations, Temperatures, Forces, Oil flow, particles, and velocity

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Genius CM IoTPlant wide operating Condition Monitoring System

! Predict equipment failure and increase OEE.


All rights reserved © Bernhard Steenken

• Early identification of process deficiencies and unsuitable production parameters

• Automatic process supervision and guided operator support

• Increasing transparency and visibility of all process and production related data across the whole process chain

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Higher plant availability and yield!


Plant and Process SupervisionPlant wide, in-time production control

!The Learning [Steel] Plant | Future Steel Forum 2019, Budapest

October 2, 2019

All rights reserved © Dr. Markus Reifferscheid

Challenges

• Relevant alarms are missed

• Inhomogeneous automation infrastructure

• Unused machine information

Solution

• Single source of truth for all machines

• Intelligent analysis and enrichment with additional information

Technical advantages

• Be informed about relevant alarms immediately

• Continuous development keeps you up to date

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Smart AlarmAll alarms available in an intelligent and clear interface


ROI usually less than 6 months!!

All rights reserved © Dr. Markus Reifferscheid

Allows fast and easy access to up-to date documentation any where and any time.

• Online access to information of machines, components, and other equipment

• Online order initiation from compaby stock or SMS

• More than 200.000 SMS standard components accessable

20

*) Based on experience by Kingsblue. Actual savings can vary.

! 1 – 2 % increase in equipment availability*


eDocFull equipment as digital web service



Interaction of Smart Alarm, Genius CM and eDoc


Smart AlarmAlarm-Management

Machine condition• Prevent downtime• Faster troubleshooting• Optimization of

maintenance

eDocDocumentation

Online documentation• Save time when

searching for documentation

• More reliable spare part identification

Detail analysis of the error condition• Optimized maintenance

intervals• Documentation of the

measures• KPIs of maintenance

efficiency

Genius CMCondition Monitoring

Spare parts list• Faster order• Fewer errors• More transparency


All rights reserved © Dr. Markus Reifferscheid22October 2, 2019

Predictive Maintenance Solutions


Maintenance Strategies


23

The Learning [Steel] Plant | Future Steel Forum 2019, Budapest

MaintenanceActivities

Preventive

Reactive

Condition-basedMaintenance (CBM)

PredictiveMaintenance (PdM)

Time-basedMaintenance (TBM)

Usage-basedMaintenance (UBM)

EmergencyMaintenance

Breakdown/AlarmMaintenance (BDM)

Maintenance Prevention

Exchange based on expected lifetime estimated by prediction models

Exchange carried out at predetermined intervals of time

Exchange carried out at predetermined intervals of usage

Unexpected exchanges of equipment that is under CBM, UBM and TBM maintenance

Exchange of equipment deliberately operated to failure / redundant equipment

Redesign equipment as basis for optimum maintainability / reduction or elimination of maintenance tasks

Periodic/continuous monitoring of asset condition to determine if intervention is warranted

October 2, 2019


Role of Preventive Maintenance


October 2, 2019

CMMS

Equipment Info(BoM, drawings, documents etc.)

ERP

Maintenance Action

DigitalDigital

Smart Maintenance Management System

ReactiveMaintenance

PreventiveMaintenance

Stock

Maintenance Documentation

• Partners• Prices• Availability• Contract

Status • History• Remote

Service• Expert

support

Market

Condition-based Maintenance

PreventiveMaintenance Actions

Level 1

Level 2

Level 3

Predictive Maintenance

Rule based Maintenance R1: Flush once a weekR2: Replace after 10 .000 tons productionR3: ….R4: ….


Applicability of Predictive Maintenance


October 2, 2019

Failure Type Failure Pattern Occurence,Percent

Applicability of Maintenance Strategies

Wear outfailure

Randomfailure

Infant Mortality

Time/Usage basedMaintenance

Condition basedMaintenance

PredictiveMaintenance

11 %

21 %

68 %

ApplicabilityHigh Medium Low


Component Selection


October 2, 2019

Criteria Rational

Failure frequency

Measurability ofchanging conditions

PdM requires enough data prints to provide basis for correlation calculations

PdM requires operating parameters (temp., pressure, etc.) to be measurable

< 5 failures across plant

Sensors cannot be installed on/near component

> 30 failures across plant

Sensors are already installed

All critera need to be met

Impact on production/service

Collateral damage

Impact on safety

PdM potential increases with impact of failure on production or service level

PdM potential increases with necessity and cost of replacing adjacent components

PdM potential increases with risk of injury from unexpected failure

No impact or impact on low-cost components

Failure does not affect prod. or service levels

Low risk of injury from unexpected failure

High impact or impact on high-cost components

Failure stops prod., interrupts service

High risk of injury from unexpected failure

Min. one criterion needs to be met

Scalability within/across plants

Price and availability of spare parts

Time to repair

Duration of outage

Time dependance of failure mode

More potential if same critical component in more than 1 machine

PdM potential increases with the costs for spare parts, logistics and storage

PdM potential increases with difference between time to repair when replacement is planned vs. unplanned

PdM potential increases with outage duration after unexpected failure

PdM potential decreases with time dependance of failure

Critical compoment in one machine

Low cost

No difference

Less than 1 hour

Failure is time/usage dependent

Critical component in > 10 machines

High cost

Unplanned job > twiceas long

12 hours of more

Failure is not time dependent

Optional criteria


Solve the Tuning Problem


Tune the system, adjust alarm limits• Single machines• Various operating conditions• Copy-paste of settings will not work

Classical techniques are often based on normalization• Crest factor (cancels out overall noise level)• Sideband energy ratio for assessing gear mesh

Use machine learning algorithms to automate classification• Typically, labelled data not available supervised training cannot be used

• Need to detect the failure before it occurs for the first time

• Can enhance but not replace the classical methods



Novelty Detection in Condition Monitoring Solutions


• Unsupervised learning task• Build compact description of training data

(healthy machine)• Compare („score“) newly taken data with model• Examples:

• 1-class SVM• Auto-encoder neural networks• Gaussian mixture models picked for SMS Condition Monitoring System



Background: Gaussian Mixture Models


• Simple iterative algorithm • Start with random mean

vectors and covariance matrices

• Repeat:• Soft-assign points to

components• Adjust components

given assigned points







































Background: Gaussian Mixture Models to Score New Data


• For newly taken data x, calculate mixture density p(x)

• Points far away from the components will have low density

• Easier to look at: -log p(x)

• Note how the novel points are well within the observed range of x and y alone



Genius CM: Bearing Monitoring in Hot Rolling Mill Gear Box


Roll bearing at F1 main gearbox, input shaft Classical approach for bearing monitoring• Envelope analysis and order tracking• Extract amplitudes on forcing frequencies plus global statistics (𝐬𝐬

∈ ℝ^6)• Outer ring, inner ring, rolling element, cage, etc.• Average of effective + maximum value per revolution• Extract rotation speed and motor torque (𝐮𝐮∈ℝ^2)• One datum per rolled strip filets only



Genius CM: Gaussian Mixture Models Solution


• Training data inner ring amplitude vs. rotation speed

• Clear dependency• Other 5 features in s look similar• Mixture components (2D projection)

• Training data inner ring amplitude vs. motor torque

• Only mild dependence• Other 5 features in s look similar• Mixture components (2D projection)

October 2, 2019




new bearing

Novelty Detection• Score (unfiltered, sliding median)• Drastic increase 4 months prior to bearing exchange • Score keeps high with new bearingWhy?





Novelty Detection• Look at z-transformed features of outer ring indicates

that outer ring damage has been eliminated• non-specific increase in score check other single

features in s in GeniusCM tool

Marked in red: Sensor problems

Marked in red: Sensor problems



Genius CM: Bearing Monitoring in Hot Rolling Mill Gear BoxWhy is the score level still high?


• Order spectrum just before exchange of bearing clearly shows outer ring damage

• Order spectrum for new bearing already shows a weak rolling element damage pattern

Novelty Detection• Monitor and supervise up coming rolling element damage



Novelty detection and Predictive Maintenance in steel production plants


Conclusion• Novelty detection enhances the condition monitoring system with predictive

functions• Effort for tuning alarm limits is significantly reduced, at the same time reliability is

increased (reduced false positive)• Keep interpretability easy by

• Grounding the machine learning part on well-understood statistical features that are tailored to the specific types of machines

• z-transformation of individual features• Using the combination with classical visualization techniques



Conclusion


Takeaways

• Digital solutions generate value in in Planning, Quality, Asset, Energy, Logistics etc.

• Combine steel technology know-how with digital know how to generate max. value

• Harvesting data is one key enabler

• Hybrid Cloud Solution will be the future

• Linked platform solution will provide the digital services (internal and external)

• Machine Learning will enable or improve but not fully replace traditional approaches

in asset management



Thank you for your attention!

43

CEO SMS digital GmbH +49 211 881 6111

[email protected]

my.sms-group.com

Dr.-Ing. Markus Reifferscheid

EVP SMS group GmbH

Please contact me for further informationor a personal meeting

The information provided in this presentation contains a general description of the performance characteristics of the

products concerned. The actual products may not always have these characteristics as described and, in particular, these may

change as a result of further development of the products.The provision of this information is not intended to have and

will not have legal effect.


October 2, 2019

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