SlideShare a Scribd company logo
Reducing Time Spent by Maintenance?!
Insights in (20) minutes + Q&A
Speaker : Jules Oudmans
@UReason : Responsible for APM Deliveries
Background : Applied Physics, Mechanical Engineering & Computer Science
AI/Ind 4.0 : >25 Years/ > 8 Years
Motto : “Digital, What Else?“
About
Us Software
APM: On-Device, Edge, Cloud
20+ Years in Business
Monitoring and optimization of components,
assets and processes with data
Industry Knowhow
Operations, Maintenance, Data Architecture,
OT & IT
Software:
APM Studio
Software Platform for
developing and deploying
Industry 4.0 applications
In a low/no code
environment
Condition
Based
Monitoring
Predictive
Maintenance
Digital Twin
Advanced
Alarm
Management
Low/No Code
High accuracy
by combining
casual models
with Machine
Learning
Scalable to
1000’s of
devices
As Platform
Asset Owners
As Micro Service
OEMs & Skid Providers
What do we do every day:
Use Cases
& Business
Cases
Model
Development
Data
Collection
Hardware &
APM
Software
Projects
Manufacturing
Asset Owners
OEM
Skid Builders
OEMs
(40% of our
Projects)
(60% of our
Projects)
APM Studio
Industry 4.0 Applications
The Usual Suspects
The common list of areas where you can save time/become more efficient
CMMS: Adoption of a good CMMS system
that supports your Field Services
Planning: Optimizing your planning, scheduling
and work allocation for the asset-base and
people on the basis of the skill-set they have.
Effective RCA/FMA: Identify the root-causes,
and remediation steps in a structured fashion
Training: Skilled people for the given task
perform this in a shorter time (really?!)
Thinking outside the box
The Forgotten Ones
Using existing data from your asset base
Data Stored: Data stored in your process historians
contains the context in which the assets supporting
your processes have been used! Enrich this with the
alarm and even data/logs and work-order history!
Data Available but Unused: Many devices
communicate status information and diagnostic
information which is very often overlooked.
Source: Endress & Hauser
Namur 107:
Do Nothing Replace/Fix
Your Maintenance Strategy?
One or combination of
Do Nothing
Monitor Health
Asset
Performance
Time
Optimal
Performance
Asset
Degradation
Performance
Disruption(s) Failure
Reactive
Preventive
Predictive
Prescriptive
Scheduled Maintenance Emergency Maintenance
Predict Events On-Time Maintenance Resume Production
Monitor Health
Predict Events
Support Field
On-Time
Maintenance
Resume Production
The Required Change
Maintenance Operations Changes on the Horizon
Physical meets Digital: Maintenance will become
more digital, using OT systems/data.
Knowledge: Knowledge needs to be readily available
to the Connected Field Worker providing Decision
Intelligence on the basis of Data, Knowledge and AI
models.
New Tools: Maintenance will be managed through
new tools supporting the CFW Today
Tomorrow
The People Perspective
And organization and legislation/regulation!
Automation of inspection tasks and continuous asset
monitoring helps you to tackle the issues of:
• An aging workforce
• Attracting technical skilled and experienced personnel
• Retaining technical personnel
But regulation/legislation may prevent automation of
periodic inspections  The argument against human error
and risk is reduced with continuous insights in asset
health!
Summary
1. Use the data you already have to automate inspection tasks – Data Stored and Available but
Untapped
2. Maintenance process will change – we can’t continue to grow old together with our installations
3. Automation will make your processes more efficient and allow you to attract and retain skilled
and experienced personnel
Monitor Health
Predictive
Prescriptive
Predict Events On-Time Maintenance Resume Production
Monitor Health
Predict Events
Support Field
On-Time
Maintenance
Resume Production
Q&A
joudmans@ureason.com
Jules Oudmans

More Related Content

Similar to Reducing Time Spent in Maintenance | UReason

Capgemini Smart Plant Supervision Solution
Capgemini Smart Plant Supervision SolutionCapgemini Smart Plant Supervision Solution
Capgemini Smart Plant Supervision Solution
Capgemini
 
Building a Robust Foundation for Digital Asset Management
Building a Robust Foundation for Digital Asset ManagementBuilding a Robust Foundation for Digital Asset Management
Building a Robust Foundation for Digital Asset Management
Yokogawa1
 
Hp - 27mai2011
Hp - 27mai2011Hp - 27mai2011
Hp - 27mai2011
Agora Group
 
InfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionInfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
InfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
Avadhoot Patwardhan
 
Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017
Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017
Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017
sandhibhide
 
Critical Water and Wastewater Data Security
Critical Water and Wastewater Data SecurityCritical Water and Wastewater Data Security
Critical Water and Wastewater Data Security
WaterTrax and Linko Technology
 
Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...
Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...
Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...
Precisely
 
Presentation
PresentationPresentation
Presentation
webhostingguy
 
1415 reed
1415 reed1415 reed
Internet of things industrial view
Internet of things   industrial viewInternet of things   industrial view
Internet of things industrial view
Nilesh Trivedi
 
Real-Time Analytics for Industries
Real-Time Analytics for IndustriesReal-Time Analytics for Industries
Real-Time Analytics for Industries
Avadhoot Patwardhan
 
Kaseya: 5 Tips for Healthcare IT Directors
Kaseya: 5 Tips for Healthcare IT DirectorsKaseya: 5 Tips for Healthcare IT Directors
Kaseya: 5 Tips for Healthcare IT Directors
Kaseya
 
Certero ITAM Review Tools Day
Certero ITAM Review Tools Day Certero ITAM Review Tools Day
Certero ITAM Review Tools Day
Martin Thompson
 
White Paper EAM2.0
White Paper EAM2.0White Paper EAM2.0
White Paper EAM2.0
Amul Patel
 
Demystifying internet of things
Demystifying internet of thingsDemystifying internet of things
Demystifying internet of things
Global Business Intelligence
 
Reimagine Service Delivery using IoT and AI
Reimagine Service Delivery using IoT and AIReimagine Service Delivery using IoT and AI
Reimagine Service Delivery using IoT and AI
Ashish Saxena
 
Advanced Reality in Industry Training Ecosystems
Advanced Reality in Industry Training EcosystemsAdvanced Reality in Industry Training Ecosystems
Advanced Reality in Industry Training Ecosystems
Carlos J. Ochoa Fernández
 
What is Predictive Maintenance? Learn Its Benefits & Role of Industrial IoT
What is Predictive Maintenance? Learn Its Benefits & Role of Industrial IoTWhat is Predictive Maintenance? Learn Its Benefits & Role of Industrial IoT
What is Predictive Maintenance? Learn Its Benefits & Role of Industrial IoT
Embitel Technologies (I) PVT LTD
 
Effectively Managing Your Historical Data
Effectively Managing Your Historical DataEffectively Managing Your Historical Data
Effectively Managing Your Historical Data
Callidus Software
 
Artificial Intelligence Application in Oil and Gas
Artificial Intelligence Application in Oil and GasArtificial Intelligence Application in Oil and Gas
Artificial Intelligence Application in Oil and Gas
SparkCognition
 

Similar to Reducing Time Spent in Maintenance | UReason (20)

Capgemini Smart Plant Supervision Solution
Capgemini Smart Plant Supervision SolutionCapgemini Smart Plant Supervision Solution
Capgemini Smart Plant Supervision Solution
 
Building a Robust Foundation for Digital Asset Management
Building a Robust Foundation for Digital Asset ManagementBuilding a Robust Foundation for Digital Asset Management
Building a Robust Foundation for Digital Asset Management
 
Hp - 27mai2011
Hp - 27mai2011Hp - 27mai2011
Hp - 27mai2011
 
InfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionInfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
InfoSphere Streams toolkits :Real-Time Analytics on Data in Motion
 
Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017
Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017
Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017
 
Critical Water and Wastewater Data Security
Critical Water and Wastewater Data SecurityCritical Water and Wastewater Data Security
Critical Water and Wastewater Data Security
 
Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...
Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...
Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...
 
Presentation
PresentationPresentation
Presentation
 
1415 reed
1415 reed1415 reed
1415 reed
 
Internet of things industrial view
Internet of things   industrial viewInternet of things   industrial view
Internet of things industrial view
 
Real-Time Analytics for Industries
Real-Time Analytics for IndustriesReal-Time Analytics for Industries
Real-Time Analytics for Industries
 
Kaseya: 5 Tips for Healthcare IT Directors
Kaseya: 5 Tips for Healthcare IT DirectorsKaseya: 5 Tips for Healthcare IT Directors
Kaseya: 5 Tips for Healthcare IT Directors
 
Certero ITAM Review Tools Day
Certero ITAM Review Tools Day Certero ITAM Review Tools Day
Certero ITAM Review Tools Day
 
White Paper EAM2.0
White Paper EAM2.0White Paper EAM2.0
White Paper EAM2.0
 
Demystifying internet of things
Demystifying internet of thingsDemystifying internet of things
Demystifying internet of things
 
Reimagine Service Delivery using IoT and AI
Reimagine Service Delivery using IoT and AIReimagine Service Delivery using IoT and AI
Reimagine Service Delivery using IoT and AI
 
Advanced Reality in Industry Training Ecosystems
Advanced Reality in Industry Training EcosystemsAdvanced Reality in Industry Training Ecosystems
Advanced Reality in Industry Training Ecosystems
 
What is Predictive Maintenance? Learn Its Benefits & Role of Industrial IoT
What is Predictive Maintenance? Learn Its Benefits & Role of Industrial IoTWhat is Predictive Maintenance? Learn Its Benefits & Role of Industrial IoT
What is Predictive Maintenance? Learn Its Benefits & Role of Industrial IoT
 
Effectively Managing Your Historical Data
Effectively Managing Your Historical DataEffectively Managing Your Historical Data
Effectively Managing Your Historical Data
 
Artificial Intelligence Application in Oil and Gas
Artificial Intelligence Application in Oil and GasArtificial Intelligence Application in Oil and Gas
Artificial Intelligence Application in Oil and Gas
 

Recently uploaded

一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
nedcocy
 
TIME TABLE MANAGEMENT SYSTEM testing.pptx
TIME TABLE MANAGEMENT SYSTEM testing.pptxTIME TABLE MANAGEMENT SYSTEM testing.pptx
TIME TABLE MANAGEMENT SYSTEM testing.pptx
CVCSOfficial
 
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
学校原版美国波士顿大学毕业证学历学位证书原版一模一样学校原版美国波士顿大学毕业证学历学位证书原版一模一样
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
171ticu
 
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...
PIMR BHOPAL
 
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
Gino153088
 
Computational Engineering IITH Presentation
Computational Engineering IITH PresentationComputational Engineering IITH Presentation
Computational Engineering IITH Presentation
co23btech11018
 
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
Yasser Mahgoub
 
22CYT12-Unit-V-E Waste and its Management.ppt
22CYT12-Unit-V-E Waste and its Management.ppt22CYT12-Unit-V-E Waste and its Management.ppt
22CYT12-Unit-V-E Waste and its Management.ppt
KrishnaveniKrishnara1
 
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
bijceesjournal
 
Digital Twins Computer Networking Paper Presentation.pptx
Digital Twins Computer Networking Paper Presentation.pptxDigital Twins Computer Networking Paper Presentation.pptx
Digital Twins Computer Networking Paper Presentation.pptx
aryanpankaj78
 
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURSCompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
RamonNovais6
 
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf
Yasser Mahgoub
 
Mechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering on AAI Summer Training Report-003.pdfMechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering on AAI Summer Training Report-003.pdf
21UME003TUSHARDEB
 
ITSM Integration with MuleSoft.pptx
ITSM  Integration with MuleSoft.pptxITSM  Integration with MuleSoft.pptx
ITSM Integration with MuleSoft.pptx
VANDANAMOHANGOUDA
 
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Sinan KOZAK
 
Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...
bijceesjournal
 
Data Driven Maintenance | UReason Webinar
Data Driven Maintenance | UReason WebinarData Driven Maintenance | UReason Webinar
Data Driven Maintenance | UReason Webinar
UReason
 
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
IJECEIAES
 
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by AnantLLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Anant Corporation
 
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoringEmbedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
IJECEIAES
 

Recently uploaded (20)

一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
 
TIME TABLE MANAGEMENT SYSTEM testing.pptx
TIME TABLE MANAGEMENT SYSTEM testing.pptxTIME TABLE MANAGEMENT SYSTEM testing.pptx
TIME TABLE MANAGEMENT SYSTEM testing.pptx
 
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
学校原版美国波士顿大学毕业证学历学位证书原版一模一样学校原版美国波士顿大学毕业证学历学位证书原版一模一样
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
 
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...
 
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
 
Computational Engineering IITH Presentation
Computational Engineering IITH PresentationComputational Engineering IITH Presentation
Computational Engineering IITH Presentation
 
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
 
22CYT12-Unit-V-E Waste and its Management.ppt
22CYT12-Unit-V-E Waste and its Management.ppt22CYT12-Unit-V-E Waste and its Management.ppt
22CYT12-Unit-V-E Waste and its Management.ppt
 
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
 
Digital Twins Computer Networking Paper Presentation.pptx
Digital Twins Computer Networking Paper Presentation.pptxDigital Twins Computer Networking Paper Presentation.pptx
Digital Twins Computer Networking Paper Presentation.pptx
 
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURSCompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
 
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf
 
Mechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering on AAI Summer Training Report-003.pdfMechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering on AAI Summer Training Report-003.pdf
 
ITSM Integration with MuleSoft.pptx
ITSM  Integration with MuleSoft.pptxITSM  Integration with MuleSoft.pptx
ITSM Integration with MuleSoft.pptx
 
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
 
Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...
 
Data Driven Maintenance | UReason Webinar
Data Driven Maintenance | UReason WebinarData Driven Maintenance | UReason Webinar
Data Driven Maintenance | UReason Webinar
 
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
 
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by AnantLLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
 
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoringEmbedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
 

Reducing Time Spent in Maintenance | UReason

  • 1. Reducing Time Spent by Maintenance?! Insights in (20) minutes + Q&A Speaker : Jules Oudmans @UReason : Responsible for APM Deliveries Background : Applied Physics, Mechanical Engineering & Computer Science AI/Ind 4.0 : >25 Years/ > 8 Years Motto : “Digital, What Else?“
  • 2. About Us Software APM: On-Device, Edge, Cloud 20+ Years in Business Monitoring and optimization of components, assets and processes with data Industry Knowhow Operations, Maintenance, Data Architecture, OT & IT
  • 3. Software: APM Studio Software Platform for developing and deploying Industry 4.0 applications In a low/no code environment Condition Based Monitoring Predictive Maintenance Digital Twin Advanced Alarm Management Low/No Code High accuracy by combining casual models with Machine Learning Scalable to 1000’s of devices As Platform Asset Owners As Micro Service OEMs & Skid Providers
  • 4. What do we do every day: Use Cases & Business Cases Model Development Data Collection Hardware & APM Software
  • 5. Projects Manufacturing Asset Owners OEM Skid Builders OEMs (40% of our Projects) (60% of our Projects) APM Studio Industry 4.0 Applications
  • 6. The Usual Suspects The common list of areas where you can save time/become more efficient CMMS: Adoption of a good CMMS system that supports your Field Services Planning: Optimizing your planning, scheduling and work allocation for the asset-base and people on the basis of the skill-set they have. Effective RCA/FMA: Identify the root-causes, and remediation steps in a structured fashion Training: Skilled people for the given task perform this in a shorter time (really?!)
  • 8. The Forgotten Ones Using existing data from your asset base Data Stored: Data stored in your process historians contains the context in which the assets supporting your processes have been used! Enrich this with the alarm and even data/logs and work-order history! Data Available but Unused: Many devices communicate status information and diagnostic information which is very often overlooked. Source: Endress & Hauser Namur 107:
  • 9. Do Nothing Replace/Fix Your Maintenance Strategy? One or combination of Do Nothing Monitor Health Asset Performance Time Optimal Performance Asset Degradation Performance Disruption(s) Failure Reactive Preventive Predictive Prescriptive Scheduled Maintenance Emergency Maintenance Predict Events On-Time Maintenance Resume Production Monitor Health Predict Events Support Field On-Time Maintenance Resume Production
  • 10. The Required Change Maintenance Operations Changes on the Horizon Physical meets Digital: Maintenance will become more digital, using OT systems/data. Knowledge: Knowledge needs to be readily available to the Connected Field Worker providing Decision Intelligence on the basis of Data, Knowledge and AI models. New Tools: Maintenance will be managed through new tools supporting the CFW Today Tomorrow
  • 11. The People Perspective And organization and legislation/regulation! Automation of inspection tasks and continuous asset monitoring helps you to tackle the issues of: • An aging workforce • Attracting technical skilled and experienced personnel • Retaining technical personnel But regulation/legislation may prevent automation of periodic inspections  The argument against human error and risk is reduced with continuous insights in asset health!
  • 12. Summary 1. Use the data you already have to automate inspection tasks – Data Stored and Available but Untapped 2. Maintenance process will change – we can’t continue to grow old together with our installations 3. Automation will make your processes more efficient and allow you to attract and retain skilled and experienced personnel Monitor Health Predictive Prescriptive Predict Events On-Time Maintenance Resume Production Monitor Health Predict Events Support Field On-Time Maintenance Resume Production

Editor's Notes

  1. Hello everyone welcome to this webinar hosted by UReason. In this, short, webinar I will take you through a number of ways to reduce the time spent by maintenance allowing you to achieve higher productivity and less downtime. In summary, but don’t go yet  .. There is a big opportunity to automate certain tasks your maintenance teams carry out and be in better control. In this webinar we will have a look at what is possible and how this can make your business more attractive for your existing and new hires. I am Jules Oudmans presenting to you today I have a background in AI starting in the nineties and have been involved many times in the past 25 years in prognostic and predictive programs that ensure asset integrity for critical assets and critical processes. I have a mixed background in physics, mechanical engineering and computer science .. And my motto is alike the famous coffee one … “Digital, What Else”
  2. I work at UReason, a software company, that provides solutions for real-time condition based and predictive maintenance and I help our customers daily to use our software – from data analysis to the set-up of applications and solutions that monitor important assets and processes. At UReason we combine our domain expertise and software knowledge with our customers, and I help them from data to solutions. We have offices in Rotterdam, which we see here in the pictures, and Wokingham in the UK. Our customers are predominantly in the manufacturing and process industry and the majority of them are located in Western Europe and North Americas.
  3. Our software, APM Studio, is used at different levels in the automation pyramid Embedded – with OEMS – monitoring Faults and Risks in ‘isolation’ to the asset. An Asset can be instrumentation an actuator or a pump, compressor, filter et cetera At the Edge … processing asset data of one or multiple assets to run condition monitoring and predictive applications near to where the data is generated. AND we also work at Level-2/Level-3 where APM is used to monitor faults in relationship to the process, deployed/running on on-premise compute When deployed at Level-4 and Level 5 APM-Studio is used for optimizing the maintenance costs and planning associated to an asset base supporting a process.
  4. UReason is active in real-time condition monitoring, predictive and prescriptive maintenance. Our field of operation is from helping customers to insights into data to helping Asset Owners, OEMs and maintenance service organisations with data driven maintenance solutions. Often, we start together with our customers to define the business cases and use-cases to focus on, followed by data collection, model development and deploying the solutions into the existing OT and IT landscape.
  5. We work and deploy our software APM Studio for Manufacturing companies .. This is about 40% of our business and we work a lot with OEMs and Skid Builders. For OEMs Their Focus: is to Maintain margin, provide Data integration, New services/business models and Staying relevant for the customer. They typically focus on The Value of the Asset. For Asset Owners Their Focus is to Lower (energy) costs, Reduce planned maintenance, Reduce reactive maintenance, Stretch asset life, Balance risks and Optimize planning For Asset Owners it is all about The Value of the Asset Supporting the Process.
  6. So the topic of today is to show you how you can reduce the time spent by maintenance. Now when you go on-line and search for this you will come across a number of the Usual Suspects .. And stay with me [CLICK] as these are things most of you know already and it may be boring this next minute!! CMMSes: Computerized Maintenance Management Systems allow you to stream-line your maintenance operations, work order assignment, asset data, spare parts etc. .. There is nothing new here it is all about structure and processes to support you field services teams. Planning: Planning and matching the work for the available people is important … but off course also something you already do! A more interesting subject is effective root-cause analysis and failure mode analysis to close the loop on the work done outside and improve. Here a CMMS can be of great help if you log problem, cause and remediation information. But we find often that the CMMS is used as asset-register (what is where and what is it) and as a ticketing/work-order system. And then there is the other usual suspect training .. Skilled technicians work more efficiently. Nothing new here either?!
  7. The Usual Suspects, in our eyes, are all about improving what you already have and focus on creating a faster Formula-1 Pitstop team. You can optimize only as much as possible on what you have. Henry Ford had a nice quote on this …Which basically drills down to the issue industry often is faced with – not thinking outside the box! It wasn’t actually Henry Ford that said this .. It came from an Puck cartoon  but I like the quote and everyone knows Henry Ford and not Puck Cartoons! So we have endavour, and will continue to do so, on a different path where we show the industry what is possible with what they already have but do not see. It is not my favorite person and company, but I will quote him .. Steve Jobs who understood very well that you have to show people to convince them and that is what we do on a daily basis with great passion and motivation.
  8. Because there are other ways to reduce the time spent by maintenance on the asset base. I call them the forgotten ones in this slide, but you could also call them the unseen ones. The first is the data that is already available in your process historians/logs . This data contains the context in which the assets are used. If you have alarm and event data also available and work-order data from your CMMS this is a really good start that can already yield good result. You may not be able to detect/monitor all fault-mechanisms of your assets but it is very often a good start! In addition to this you can tap into the data that is available but unused and often not unlocked. Most of your instruments and actuators are capable of communicating diagnostic information which is often overlooked or only consulted in manual inspection rounds. Irrespective of the communication protocol of your smart diagnostic devices, of which you have many, communicate a so called namur 107 status. This status contains important information that allows you to reduce visual, on-site, inspections. The Check Function is a lower severity event that indicated that the signal was temporarily invalid. This could have multiple causes and especially when it happened multiple times it is something to check out. Maintenance Required indicates that the instrument or actuator will have a function drop soon .. Out of Specification means that the device is running outside its permissible range and as such cannot properly support the function it is required to provide. Failure: indicates a malfunction of the instrument, sensor or actuator.
  9. Now let us consider your maintenance strategy, but before I do let me first explain the graphic we see on the slide. The graphic is an asset degradation curve, called PF curve in most literature. At the start, when my asset is brand new asset-performance is optimal. Over time it degrades because of wear and tear, improper usage etc. At some point in time performance disruptions will start to occur and the likelihood of a failure, rendering your asset unable to support your process anymore is inevitable. For certain assets this is acceptable, you have a redundant (N+1) set-up, little to no process disruption and spares in stock. In this case your strategy will be reactive – or run to failure– you do nothing until the failure occurs and you then replace it or performance emergency maintenance. If you follow the maintenance instructions of the OEM or when you have a lot of experience with the asset failing and it is not so critical you run a preventive maintenance strategy you do nothing up to a certain point when scheduled maintenance kicks in which you may to too early of too late and you will still run into failures. When you monitor the data coming from the asset we talk about a predictive strategy when you also use the data and degradation models to estimate/predict when the asset is about to fail such that you can schedule the intervention and provide on-time maintenance without loosing production Advancing on predictive is a prescriptive strategy where alike predictive you continuously monitor the health and when needed schedule maintenance providing support to the Field worker. this allows you again to provide on-time maintenance and have minimal disruptions with maximum knowledge brought to the service personnel.
  10. You probably have seen a PF curve and the different maintenance strategies that I showed in the previous slide before. But nevertheless, we experience on a daily basis with our customers and new customers that there is this GAP between maintenance operations and operations. We see that Maintenance will have to change and become more OT data driven At the same time we need to bring more knowledge to the worker at the site and use data to provide Decision Intelligence and automated RCA a rise of new tools will come to you if you are not already adopting them!
  11. Automation of inspection tasks and continuous asset monitoring provides solutions to mitigate the consequences of: 1) An aging workforce 2) Difficulties in attracting technical personnel 3) Difficulties in retaining technical personnel. There is offcourse a legal perspective: some legislation/regulation discourages automation of periodic inspections. Automation reducing the human error and provides a continuous insight in safety of operations related to asset health supporting the functions
  12. To summarize you can greatly improve the time spent by your maintenance by – Using the data you already have .. -- Change the maintenance process -- Automate the tasks
  13. Ok that was the last question, thank you very much from my side for listening and asking interesting questions. Here are my contact details and please note that after the webinar you will receive the slides and a link to the replay. Again we appreciate your feedback .. please leave us your feedback via the evaluation form – see the link in the chat window. I wish you a nice day and maybe we'll see you in one of our next webinars.