This webinar discusses predictive and preventive maintenance strategies. It will feature presentations from Harry Kohal of Eagle Technology and David Gell of VIE Technologies on the key factors in maintenance, the differences between condition monitoring and predictive maintenance, and how machine learning and AI are improving predictive maintenance. The webinar will also explore case studies on reducing maintenance costs and improving asset availability through predictive analytics of vibration data. While predictive maintenance provides benefits, challenges remain regarding integrated solutions, training of AI systems, connectivity, cybersecurity and organizational adoption.

1. LIVE WEBINAR
Beyond Predictive &
Preventive Maintenance
Wednesday, October 30th, 2019

2. Harry Kohal
2
Vice President of Business Development
Eagle Technology, Inc.
David Gell
Chief Marketing Officer
VIE Technologies
Guest SpeakerPresenter
David built his expertise as a serial entrepreneur with executive positions in
marketing, R&D and operations in wireless, telecom and manufacturing
sectors (Hewlett-Packard, Solectek, Cygnus Broadband, WiLAN and Olympus
Sky). He is responsible for more than 30 products and service launches, has
more than 65 patents and holds a master’s degree in controls engineering
(Carnegie-Mellon).
Harry Kohal is a Life-time member and past president of the Wisconsin
Association of Computer Crime Investigators. He has been in IT and IT
consulting for over 35 years, starting in the US Air Force. Harry joined
Eagle Technology in June of 2006 and is currently Vice President of
Business Development, headquartered in Mequon, WI. Harry has a
Bachelors Business and Marketing from Marian University, and a
Master’s Degree in Liberal Studies from the University of Wisconsin –
Milwaukee.

3. 3
The key factors involved in
maintenance strategies

4. Key Factors
4
The cost of equipment failure
The ease of monitoring the equipment
Corporate culture

5. What is the difference between
condition monitoring and true
predictive maintenance?

6. Condition Monitoring
TITLE
6
Condition Monitoring is
the continuous monitoring of the
equipment’s state and operating
parameters, usually through dedicated
sensors and monitoring tools.

7. Predictive Maintenance
7
Predictive Maintenance refers to planning
corrective maintenance based on
predictions about the evolution of a system.
These predictions are based on data obtained
through Condition Monitoring, and on system-
specific knowledge.

8. Preventive Maintenance and
AI Machine Learning
Machine learning is paving the way for smarter and faster
ways to make data-driven decisions in Predictive
Maintenance (PdM).

9. 9
Facilities requires a digital platform to capture,
store and analyse data generated by control
systems and sensors on equipment connected via
the IIoT.
Preventive Maintenance (PM) is key for
improving uptime and productivity, so greater
predictive accuracy of equipment failure is
essential with increased demand.

10. 10
How CMMS fits into IIoT
The Industrial Internet of Things (IIoT) is connecting
people
data and
intelligent machines
while shaping the future of equipment reliability.

11. 11
Reactive to Predictive Maintenance
Real-Time Analytics for Informed Decisions
Improved Data Quality and Quantity
IIoT delivers crucial information to the maintenance management team and increases
their efficiency in the following ways:
The contributions of IIoT to maintenance management include reduction of cost and effort along
with improved uptime of assets.
How CMMS fits into IIoT

12. Getting the most from
predictive maintenance

13. Airline Maintenance Strategies
Courtesy of Exsyn Aviation Solutions
Predictive Maintenance
Answers the what, when and whyfor
the optimal and personalized maintenance of
each asset
What maintenance or repair actions are needed?
When does it need to be done?
Why does the asset need maintenance?
(i.e. underlying defect)

14. Predictive maintenance is an optimal approach
Benefits of Predictive
Maintenance include
 Reducing maintenance costs
 Improving asset availability
 Enhancing governance

15. 80 years of maintenance evolution
Waddington effect
WWII – Royal Air Force
Yellow-handle
assessment
Vibration via precision,
digital assessment
Predictive Maintenance
Human sensor
Human analyst
One-shot digital sensor
Human analyst
24x7 digital sensor
AI analyst

16. Vibration-based predictive analytics is based on
the science of rotating machinery
But historically, required experienced
human analysts to properly assess = $$$

17. Continuous vibration monitoring was used only on
the most expensive, mission critical assets
Less expensive/critical assets
were assessed only a few times
per year, if at all
Titan 130 Gas Turbine Generator Set
Courtesy of Solar Turbines

18. Today, advances in technology are shifting
this landscape
Low cost, high precision sensors
Ubiquitous wireless connectivity
Cloud processing and storage
AI/ML
Enables easy-to-deploy, cost-
effective predictive solutions
Industry 4.0 and Industrial IoT

19. Example: reducing maintenance
cost for a 1.1MW UPS generator
Facility: Banking Data Center
Scheduled + go/no-go vibration
sensor with thresholding
Existing Maintenance
OPA 7x24 monitoring &
prognosis
Personalized Maintenance
Savings = $35k / year on a single asset
ROI = 12-14X
 Grease bearings ~monthly
 Panic-based maintenance
 Alarm shutdowns
 Grease bearings only
when needed
 Advanced prognosis
 No panic
Cage Defect frequencies showing no
maintenance required (1x, 2x, 3x FTF)

20. Challenges remain
1. Many solutions remain piecemeal – you may become the integrator
2. Most AI predictive solutions are essentially trained pattern recognition. How
long to train?
3. Connectivity in industrial environments remains difficult
4. Cybersecurity is paramount
5. Organizations have different attitudes towards “digital transformation”
Be careful of technology’s hype machine

21. Important questions to ask
Solution
 Does the solution provide a true prognosis or just
raw data?
 Will this solution provide immediate predictive
value? Or do I need to train it?
 Is this solution end-to-end complete?
 Are the sensors easy to deploy? Easy to maintain?
 Do I need to get my IT group involved?
 Is this solution secure?
My organization
 What are my goals in deploying a predictive
solution?
 Do I have support across the organization?
 How can I mitigate areas of resistance?
 How can I show value as rapidly as possible?
 How well and how fast will the solution
scale?

22. A predictive maintenance regimen can provide powerful new capabilities for
your operation
 Reduced maintenance cost by 5-15%
 Enhanced asset availability by 10-30%
 Improved governance
Solutions are available today delivering these benefits
Adoption requires thoughtful consideration around technology and
organizational challenges. Pick your partners carefully.
Conclusions

23. Get In Touch With Us
23
Eagle Technology, Inc.
Harry Kohal
Phone: +1.262.241.3845
Email: harryk@eaglecmms.com
Web: www.eaglecmms.com
VIE Technologies
David Gell
Phone: +1.858.442.4244
Email: dgell@vietechnologies.com
Web: www.vietechnologies.com