Transform your data

into solutions

Who is PEPITe ?DATAmaestro Advanced Analytics Platform

Applications for Yield, OEE, productivity, quality, energy optimisation, predictive

maintenance

DEDICATED APPLICATIONS

Performance optimization based

on advanced analytics and

change management in

operations

Production performance

monitoring and optimization

in aquaculture (salmon

industry)

FindIT App

Performance & health care for

assets (wind turbines, steam

turbines, CNC, etc.)

Wintell App

SERVICES

OPTImaestro projects

A global network of sales and technical partners

- Cypher Energy- Pangolin Associates (Looking for pilots)

ITC Infotech (India)

Confipetrol AndinaPdM Xperts (Peru)

Energies (Col)

- Indea, Techflam, DNV (W. Europe)

Franz Inc. (Asia)

Otelo International (Mx)

Texas A&M University(USA)

PowerMar(US/CAN)

PROSPECTIVE

SAG (JPN)

BelgoBras

Sales partnerTechnical partner

La

st M

od

ified

11

/27

/20

17

4:0

9 P

M R

om

an

ce

Sta

nd

ard

Tim

eP

rinte

d

6McKinsey & Company

Industry 4.0 will impact operations through mainly four key disruptive

technologies

Disruptive

technologies

Industry 4.0

Advanced

analyticsBig data

Human

machine

interface

Additive

Manufacturing

and Advanced

Robotics

Industry 4.0

Capacity &

Quality Increase

Lean

Operations

Mass

Production

Source : Mckinsey & Company

PEPITE CORE BUSINESS

SOURCE: Electricity Consumers Resource Council estimated the cost of August 213 blackout in US between $4.5 and $8.2 billions

Type of project Impact

Optimize use of energy in exothermic processes

Reduce energy costs by 15%Chemicals

Predict and understand root causes of breaks in paper sheets

Reduce shutdowns and increase OEEof 5%Paper making

Use historical data to predict in real time the quality of the steel

Increase yield and reduce scrap by 5%Steel making

Forecast dynamic security of transmission grid

Avoid costly curtailment of loads or generations; in the worst case avoid black-outs (several billions1)

Electrical network

Collect data from hatcheries and provides analytics features to decrease malformation rates

Reduce malformation rates of fish by 20%Hatcheries

Predictive Maintenance project to enhance O&M services

Reduced unplanned down timeCost saving of 10% (lower insurance costs)Wind mills

Analyse drilling operation data to increase ROP

Faster drilling and less downtimes due to well head failure

E&P drilling operations

What is Artificial Intelligence ?

• Artificial Intelligence is a key technology to enable advanced analytics in a big data world

• Artificial Intelligence concepts were invented by Alan Turing; in 1950, he published a paper called “Computing machinery and intelligence.” founding the root of AI.

• There are two main technologies in AI :

Expert systems

Using a knowledge

base, computer uses

an inference engine to

take decision

Machine learning

Using historical data :

1. the computer learns

relationships

(knowledge)

2. the computer uses

what he has learnt to

take decisions

Evolution technology – a new world of opportunities

DMpack is a cheap portable device with embedded services for data collection, data storage, advanced analytics, dashboarding

IBM State of the art : 1956 DMpack State of the art : 2017

Pilot ProjectFrom identified opportunities, a pilot is

selected with clientAnalytics is applied to solve the case

(OPTImaestro)

How to start digital analytics journey ?

STEP 1Digital Data Diagnostic

STEP 2 : Proof of Value Project

STEP 3 : Analytics Capability Transfer

Digital Data DiagnosticWhere is the data ?

What are the data flows ?How is it organized ?

Install DMpack on-siteCan we stream data to the cloud ?

What are the savings opportunities ?How big are these opportunities ?

How to leverage opportunities with analytics ?

Client with Analytics Capability Pilot enables client training in advanced

analytics capabilitiesClient can implement analytics projects using

DATAmaestro technology

1-2 Months

2 – 6 months

SaaS access fee per user per year

(*) Indicative estimates; can vary depending on application complexity

OPTImaestro project frameworkSTEP 2 : OPTImaestro project to optimize plant operation

DATAmaestro Platform setup/configuration

Clo

ud

DATAmaestro Platform

DATAmaestro CollectorConnect, collect

DATAmaestro LakeStore, merge, resample

DATAmaestro AnalyticsVisualise, analyse, diagnose, predict

DATAmaestro DashboardsReal-time monitoring

Industrial/Manufacturing Process

Collectors, Lake, Analytics and Dashboards are web services that can be installed in any secured cloud infrastructure (such as AWS, Azure, etc.).PEPITe can also provide its own secure cloud service.

Cloud based setup

Cloud based infrastructure In order to leverage the power of big data infrastructure, DATAmaestro can be also integrated in 3rd party big data infrastructure.

Integration with Big Data infrastructure

Cloud based infrastructure DATAmaestro can be installed on customer premises in a few days with the DMpack.

On premises setup

OPC UA DB/Historian native API

DM

pac

k

Plant Site

Self service analytics with DATAmaestro access

DATAMaestro is flexible and can be adjusted to comply with clients policy on data access and security

IiIIoT

Performance for Assets

Turning data into actionable intelligence

www.performanceforassets.com

Introduction

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

Production processes continuously generate

massive amounts of data, but..

“On average, between 60% and 73% of all data

within an enterprise goes unused for analytics” - Forrester

Today’s Industrial Challenges

Moreover traditional condition monitoring systems give

a very narrow view on the behaviour of your asset / process

To broaden the view on process behaviour unlimited other (structured and unstructured) data sources are available

and potentially more relevant.

Detect anomaly: identification only

Diagnose: why does anomaly happen?

Prognose: how does anomaly evolve and when to take action?

Combining process / asset knowledge with data analysis is the key to success!

Actionable intelligence for performance optimization & predictive maintenance

Immediate actions in safety, continuity, reliability and recovering of performance

Mid-term actions in planned intervention, replacement planning, savings, increase of performance

Long term actions in improved procedures, investment strategy, cash out planning

Our approach: Methodology

Our approach: Hybrid model

Online condition monitoring with global alarms + human analysis

Physical models

+

Correlation of Historical process data

+

Additional benefitWhen applied to multiple similar machines (fleet), new knowledge is applied to avoid the same failure in these machines too

!The P4A Hybrid model

Machine learning ♻ Hybrid model combining approaches of models type 1,2 and/or 3 to optimize machine-learning and models qualityand put online with the current data of the specific machine

PREDICTIVE

MAINTENANCEPERFORMANCE

OPTIMIZATION

COST REDUCTION & REVENUE INCREASE

€

€

Our goal: Providing actionable intelligence for…

ENERGY SAVINGSCOMPENSATION OF

LOSS OF KNOWLEDGE

Case studies

Case: Improved production uptime & reliability

Goals: – Determine the tool wear and provide real-

time feedback to operator– Early detection of degradation: rail bearing,

ball screw

P4A solution– Instrumentation : currents, vibration,

temperatures– Realtime analysis

• Signal processing : phase detection, filtering, enveloping

• Data analytics : ISHM

Case: Improved production uptime & reliability

Operation optimisationSignal processing

Operation optimisationData analytics

Tool wear indicator

Raw motor current

Machining phase detector

Cumsum of ISHM distance

Cause identification

Predictive maintenanceVibration signature

– Mixers (predictive maintenance)

– Steam network (energy savings)

– Steam turbine (predictive maintenance & performance)

– CNC machine (predictive maintenance & performance)

– Wind turbines (predictive maintenance & performance)

– High voltage motor (predictive maintenance)

– Transformers (predictive maintenance )

Some projects

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

creating value from data

Machine & process monitoring in the age of big data

18/04/2018

helps you create value from your data

6Years of Expertise

11People

Data Science

AI – Machine Learning

Prescriptive Analytics

Optimization algorithms

Data and Process Mining

Engineering

PID control

Vision and Sensors

Physics - Mechatronics

Advanced Process Control

Scope

Consulting and Services

End to end data projects

Software products

Custom made software

OUR SERVICES ARE USED FOR…

Process Improvement

Process control improvements to:

- resolve quality problems

- reduce cost

- improve stability and safety

Smart Machines

Software and sensor selection for:

- inspection robots

- sorting machines

- measurement devices

Advanced Analytics

Prescriptive Analytics for:

- knowledge discovery

- decision support

- root cause analysis

Anomaly Detection &

Analytics for Machine

Sensor Data & Log Files

ANOM LYYA

Traditional monitoring

2. Descriptive statistics

3. Dedicated sensors 4. KPI’s and dashboards

1. Hand-written rules

6

What changed?

More data is available- Sensors, software logs, contextual data,

lab measurements…- Easier to collect (IoT etc)- No storage / speed limits

Machine Learning and AI are mature techs

Some problems with traditional monitoring

• You can’t write rules for every issue:• there’s no time

• you don’t know what could go wrong

• Problems happen in the software execution / in the communication with someone else’s equipment.

• Not every issue is a vibration.

• Statistical analysis doesn’t always work (highly dynamical or complex signals)

Solution: Unsupervised Anomaly DetectionAI learns what’s normal by analysing (all) the data, no human input.

Unique capabilities

Yazzoom has built on top of open source Big Data technologies like Logstash, Kafka, Kibana and Elastic Search a powerful reusable and extendible technological solution with some unique capabilities:

Process mining: The ability to learn from the various heterogeneous data streams statistical models that describe how the machines, devices and systems have been used and which process steps were executed by those systems.

Context dependent anomaly detection: The ability to detect in real-time anomalies or abnormal process execution in both numerical data and log files, while taking into account the context in which the system operates.

Compared to traditional monitoring

Detect unknow problems, abnormal trends and patterns,

external sources

Detect problems that are not vibrations

Scale easily to more data, new sensors, changes, variable components or subsystems…

Detect issues in software execution in advanced machinery

Handle complex signals with better detection of

issues and less false alarms

Take context into account (machine state,

temperature, raw material properties, recipe)

Some highlights of AI-based anomaly detection- Compared to traditional systems:

- Can be used to determine where to add extra sensors / get extra data: the algorithms can pinpoint where they lack information to be accurate.

- Can be used for enhanced (cyber)security

1 minute to find the cause of an issue

Root cause analysis of downtime in 1min instead of 20.

1 hour early warning

Alarm raised before the end user noticed problems and contacted tech

support.

(Cyber)Security ?

Several recent Industrial (cyber)sabotage involved fooling traditional monitoring systems by feeding them fake sensor readings.

AI can detect that one sensor reading is not realistic given the value of all others.

Much harder to fool than traditional monitoring.

Who uses ?• Industrial production lines (continuous and

batch processes, robot-powered discrete

manufacturing)

• Medical imaging equipment

• Network and telecom infrastructure

• IoT-enabled utilities networks

• Power plant systems

• Consumer connected devices…

“Machine” users or manufacturers

• PoC

• Pilot

• Full Depolyment

…

Some misconceptions about unsupervised Anomaly Detection…

Doesn’t replace traditional approaches. It comes in complement.

Can use human input: learn faster, get feedback and become better.

Doesn’t need a supercomputer to run.

Doesn’t need to be in the cloud.

Big data, small project

PoC

Small subset of data, offline.

Goal: validate the presence of value in

the data

Typically 2-3 weeks work

Pilot

Small deployment, single system, basic

integrationGoal: refine

requirements, enhancemodels…

Typically 2-3 weeks work

Full Deployment

All systems monitoring, full

integration.

YANOMALY deployment model

Integrates with existing monitoring / data / IoT platforms / historians no big changes.

Edge or cloud.

Example Anomaly Dashboard in Browser

This view illustrates different types of anomalies discovered by process miningon log files

The user canzoom & filter & click-through tothe original log files

(More) Value inside machine data:

R&D

Service

Operations

• Gain pre-emptive knowledge of potential problems

• More efficient investigation and troubleshooting

• Reduce the mean time to repair of your service team

• Real-time detection and even prediction of anomalies

• Avoid down-time through automatic remediation actions

• Speed up root cause analysis for faster recovery

• Fraud detection

• Learning how machine/plant/utility is used

• Higher reliability through more realistic product testing

• Better product design thanks to knowledge of real usage

www.machine-analytics.com

info@yazzoom.com

+32 9 378 02 18