MAINTENANCE IN THE FOURTH INDUSTRIAL REVOLUTION ERA

Industrial Consulting for Change

MAINTENANCE IN THE FOURTH INDUSTRIAL

REVOLUTION ERA


INDUSTRY 4.0 Thoughts and proposals


Words and concepts

Big data – big amount of data that needs non conventional analysis tools to be

processed

Data mining - automatic or semi- automatic methods aimed to find correlations or

patterns from big data

Machine learning - performance improvement of software by self learning

Cognitive technologies – technologies that implie deeply the cognitive processes and

thoughts of their users

Cognitive computing – human – like man to machine interaction (IBM Watson)

Cloud computing – informatics resources access through the internet


Words and concepts

Virtual reality- emulation of sensitive reality by machines

Deep learning – part of machine learning aimed to take out information from a set of

data, using efficient algorithms.

Internet of things (iot) - extension of the internet to physical objects, so that they

become capable among them and men.

Industrial internet of things (iiot) – part of iot concerning industrial “objects” only.

Cyber security – security of data exchanged om the internet.


Areas of interest

industry

Product development

Design

Production

Maintenance

Logistics

Energy

Energy efficiency

Smart grid

Agricolture and environment

Sustainability

Trasportation

Autonomous guide vehicle

Tertiary

Consultancy and services

Finance (banks assurances)

Media (journalism)

Public administration

Building

Buildings

Domotics

Health

Hospitals

Telemedicine

Army

Attack, défense, logisticsupport


The three previous industrial revolutions

The three previous industrial revolutions were triggered by

new technologies

then they were beared by

organizational models

and then they had outcome

social - economical

and the fourth?



Source: Forschungsunion, acatech, Abschlussbericht Arbeitskreis Industrie 4.0



revolution period technologis organization outcome

1^ 1760/70 -

1830

Steam machine First industrial

organizations

From farmer to

workman

2^ 1860/70 –

early C20th

Electricity and

chemical products

Fordism,

Taylorism

Rise up of

mass

production

3^ 1950 -

today

Electronics and

informatics

Toyota

Production

System

Service

society

4^ Today Digitalization and

ICT

? Cyber-physical

products


Melting pot of know - how

TRADITIONAL

INFORMATION

&

TELECOMMUNICATION

STATISTICS

(Mathematics)

INDUSTRY

4.0


Maintenance outcome – strategical planning (life cycle)

Consistancy between company and maintenance objectives

Identification of critical elements (machines, systems) to focus

maintenance activities:

statistical analysis (RAM, productivity, costs)

Montecarlo simulation (under development in FESTO)

Identification e quantification of KPI’s

Choice of maintenance policies

FMEA

cost to benefit analysis

make or buy (contracts)

Maintenance organization

Choice of CMMS

Results are on SW made

available to the client for

use


Maintenance outcome – corrective and scheduled

On site interventions support

augmented reality

interactive virtual documentation

remote support

scheduled interventions (scheduled maintenance only

Data Collection and archiving

data analysis and processing

outcome in overhauling and continuous improvement of

maintenance policy,

outcome on machine builder (Early Equipment

Management)

Cloud computing and

decision support

Data mining


Maintenance outcome – predictive

Choice of monitoring and analysis technics

Significant inputs (signals, parameter, etc.) to collect

Signal analysis and processing(time/frequency)

Setting of acquisition intervals (continuous / periodic)

Setting of sensors positioning locations

Definition of notification criteria (sms, e-mail, etc.)

Definition and optimization of notification thresholds

Reporting and management interfaces of operating systems (CMMS; other sw)

SW available to client for

statistical analysis of

data, Smart Reporting

System


Maintenance outcome – predictive

Data acquisition and analysis

Data conditioning and processing

Data comparison vs thresholds

Triggering of warnings

Support to diagnosis and prognosis

SW available to client for

data analysis ,

development of neural

networks, reports, useful

life evaluation.


maintenance outcome – measurements and improvement of machine

performances

Big data

Data mining

Machine learning

Analytics

Cloud computing

Advanced maintenance engineering

Data repository

Failure data analysis and RCA

Suggestion of next machine design

improvement and organizational model

Design / optimization of diagnostic and

prognostic model


maintenance outcome – definition and control of budget

Lay-down of budget on the basis of technical

system and organization model and of production

plan

Budget vs actual costs gap analysis and finding of

countermeasures to be verified by simulation Tools of

economical

analysis

Simulators


maintenance outcome – spare parts management

Mathemathical model of spare parts warehouse, by means of

reliability and Montecarlo simulators in order to optimize spare

parts amount

Automatic accountability of spare upload / download

Automatic coding of spares, 3D scan of spares

3D printing of particular items

Spares distribution via robots

Mathematical model

3D acquisition

Machine learning

3D printing


maintenance outcome – third part management and contracts

Cognitive computing IOT

Automatic call for service (IOT)

Computerized evaluation of offers (software Emma)

Computerized legal consultacy (digital sollicitor)

Consultancy to company (Amelia from ITsoft, Watson from IBM)


maintenance outcome – competence improvement and management

Training on virtual machines and systems

Competence gap analysis coming out of

maintenance interventions (recorded on CMMS)

and setting up of suitable training

Distance learning Virtual rality

Big data

Data mining

Training tools and equipments

(FESTO EDUCATION)


maintenance outcome – CMMS

CMMS userfriendly, eccess from popular tablet and smartphone.

Integration between CMMS and other applications (predictive maintenance,

RCM, technical – economical optimizer) to offer a global view of maintenance

management according to Physical Asset Management

Text recognition CMMS able to classify information coming from intervention

reports

Big data

Data mining

Cloud computing

Cognitive computing

Deep learning

IIOT

Cyber security


Typical scenario

DCS

Screens

Historian

Data

Local

instrumentation

Thermographies

Vibration

Measurements

Lab

Analysis


How Technology Can Help (1)

DCS Historian

Data

Local

instrume

ntation

Thermog

raphies

Vibration

Measure

ments

Lab

Analysis

Cloud

Repository


How Technology Can Help (2)

Big Data

Statistics

Root Cause

Analysis

Neural

Networks

KPI

Calculation

Object Based

Models

Notifications

CMMS

HTML

5

Web

Services

Cloud

Repository


apmOptimizer is a Decision Support System for the assets management, suitable for ISO 55000

series standard applications. apmOptimizer method works with the departments of:

Production: by plant RDB diagram, Asset availability;

Maintenance: by LORO, FTA, Asset reliability, PM, PIO, CO;

Logistic: by Spare parts selection, Transportations and Stores;

Financial: by Life Cycle Cost.

apmOptimizer presentation

* LORO= Level Of Repair Optimization from MIL -HDBK-1390 LORA

apmOptimizer is a Decision Support System for the assets management, suitable for ISO 55000 series standard applications. The apmOptimizer method works with the departments of:

Production: by plant RDB diagram, Asset availability;

Maintenance: by LORO*, FTA, Asset reliability, PM, PIO, CO;

Logistic: by Spare parts selection, Transportations and Stores;

Financial: by Life Cycle Cost.

dat

a fl

ow

D

SS

* LORO= Level Of Repair Optimization from MIL -HDBK-1390 LORA

Exemple


dat

a s

ourc

e

f

ucti

onal

ity

• Stochastic simulation of failures conditions versus time.

• Failure distribution estimation using apmOptimize software with source failure db or failure Company data.

• Reliability, Availability and Maintenance models of apmOptimizer.

• Preventive maintenance optimization with apmOptimizer.

• Inspections optimization with apmOptimizer.

• Performance models of apmOptimizer taking into account the effects of failure interruptions and storage capacity.

• Life Cycle Cost calculation with apmOptimizer for each scenario.

Short Name Full Name Source of failure rate data base

Pipe Corrosion

Pipelines bio-corrosion models, Berkeley, University.

Bloch Heinz P. Bloch, Fred K. Geitner Pratical machinery management for process plant.

RADC RADC, Not electronic reliability note book, US Department of Commerce.

NPRD Not electronic parts reliability data. Reliability. US Department of Defence.

Company Failure data from Client

OREDA Onshore & Offshore Reliability Data.

ApmOptimizer function. & data source Exemple


apmOptimizer by source data and its algorithms identifies the predicted failure and recommends the optimal maintenance time and the optimal spare ordering time.

Process Method (one) Exemple


• Pipelines (OCAB, OSDUC, CE/REDUC)

• Flow control valves (manual and motorized)

• Main pumps with motors, transfer pumps with motors, auxiliary pumps with motors

• Flotation roof tanks with oil level meters

• Pressure safety valves

• Flow rate meters, pressure meters

• Transformers of sub station

Type of assets involved in the analysis

Optimization system example Exemple


Situation before optimization study Situation after optimization study

Total Life Cycle Cost =5.938 mln USD

Availability = 97.65%; Unvailability 2.35%;

Production loss =1600 –1581 = 19 m3/h

Total Life Cycle Cost reduction for 50 years = 38.1% (45.2mlm per year)

Unavailability may be reduced by (2.35 - 1.02) / 2.35 = 56.7 %.

Performance lost may be reduced by (19-10)/19= 47,36%

Summary of achievable results applying the optimization study

Total Life Cycle Cost =3.676 mln USD

Availability = 98.98 %; Unvailability 1,02%;

Production loss =1600 –1590 = 10 m3/h

Optimization Results Exemple


maintenance outcome – early equipment management

Retrofit equipments and system (fit for 4.0 – retrofit for 4.0)

Industry 4.0 machine design

New typre of equipments to maintain, more and more robots, more

and more electronics (casual failures)

Macchines more flexible (availability and reliability of production

systems and utilities as well assume different values with respect

the actual ones)


Social and economical outcome

Production come back to western countries, but not employment

Value creation / loss

Creation / loss of employment

Distribution of wealth among people non dependent on employment

(citizenship income)

Subjects dealt with at the Economical Internatinal Forum held in Davos


What can we do – FESTO Training and Consultancy proposal

Seven new courses

Machines and systems design according to i4.0

Cyber security in industrial communications

Maintenance toward i 4.0

Machines and systems revamping according to i4.0

Smart system integration and intelligent product RFID in action

Remote troubleshooting – remote failure finding and intervention

Business model innovation in Industry 4.0

……

and then consultancy

MAINTENANCE IN THE FOURTH INDUSTRIAL REVOLUTION ERA

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