Abstract— This presentation provides a new approach in the field of vibration analysis that aims to improve the efficiency and accuracy of data analysis to produce diagnostics and prognostics by introducing digitalisation and machine learning technologies.

1. How Digitalization and Machine Learning can
advance Vibration Analysis
Paulo Cipriano
SKF

2. How Digitalization and Machine Learning
can advance Vibration Analysis
Paulo Cipriano, Global CoE for Condition-Based Maintenance, SKF

3. 1. The Digitalization Journey
2. What is Machine Learning?
3. Applying Machine Learning to Vibration Analysis
4. Digitalization and Machine Learning Challenges
5. Conclusions
Presentation outline

4. The Digitalization Journey

5. © SKF Group
Digitalization is to collect data, which put together, effectively provides useful insights to
improve the business performance. It is becoming widespread due to the confluence of
several technologies and market trends such as:
• Connectivity: Low–cost, high–speed, everything is “connectable’’.
• IP–based networking: IP has become the dominant global standard.
• Computing Economics: Moore’s law continues to deliver greater computer capacity at lower
price and lower power consumption.
• Miniaturization: Small and inexpensive sensor devices, which drive many applications.
• Data Analytics: Enabling data aggregation, correlation, and extraction of new knowledge.
• Cloud Computing: Small and distributed devices interact with powerful back-end analytic and
control capabilities.
Digitalization journey

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Digitalization technologies and impact, Mining&Metals
Source: World Economic Forum ’Digital Transformation Initiative – Mining and Metals Industry’, January 2017

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Miniaturization and Edge Computing
Cost
Technical
features
Size
IMx-S
IMx-16 Plus
IMx-1

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2017 2018 2019 and beyond
SKF Digitalization Technologies
SKF Enlight
SKF QuickCollect & SKF DataCollect
Detect
Analyze
Diagnose
Prognose
SKF Mobile
Solutions
SKF Online
Solutions
SKF Software
Solutions
Collect
Connect
SKF
Machine
Learning
DCS and
ERP
Integration
Multilog IMx-8 IMx-1
Multilog IMx-T
Multilog IMx-S Multilog IMx-W
Multilog IMx-P
Multilog IMx-R
Multilog IMx-M
Multilog IMx-C
Multilog IMx-B
SKF @ptitude Analyst SKF @ptitude Observer
Today and before
SKF Microlog & Handheld products (non-connected)
Decision Support

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SKF Digitalization platform
An end to end customer experience
Anomaly/Diagnostic/Prognostic
“Decide at which point action is triggered”
Operator & Maintenance Actions
• Plant Process Control
• Maintenance Management System
• Work Planning & Scheduling
• Vibration
• Inspection
• Operation
• Maintenance
• Lubrication
• Spare Parts
• RCA
• Smart sensors, etc
Spare Parts Supply Chain:
• Just In Time Delivery
• Inventory Optimization
MRO Optimization
Plant Benefits:
ü Increase production output
ü Reduce Cost/Ton
ü Extend asset’s life
ü Increase availability
ü Reduce MRO spend

10. What is Machine Learning?
(Predictive Analytics)

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What is Machine Learning?
Slide 11
Give computers the ability to learn without being explicitly programmed.
(Arthur L. Samuel, 1959)

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• Imagine we are given the problem to automatically distinguish between apples and
bananas
• We can collect every data we want: Images, Measurements, Metadata, …
Apple vs Banana Problem
Slide 12

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How can we distinguish between an apple and a banana or what are good features to
separate the class “apple” from the class “banana”?
• Color, Size, Weight, Shape
• DNA (costly, but it will probably work quite well)
• Country where it was harvested
Slide 13
Apple vs Banana Problem

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What are good features?
Slide 14
Features (color, size, weight) should be able to separate the Class (apple vs banana) space as
clearly as possible
Learn a
decision boundary

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Evaluation Metrics for Machine Learning
• Accuracy = true positive and negative rate.
• Precision = true positive rate.
• Recall = false negative rate.
• Predictive Positive Condition Rate = true and false positive rate.
Example:
# Assets: 10
Faults not presented (True Negative faults): 7 assets
Faults presented (True Positive faults): 3 assets

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Accuracy
Predicted Negative Predicted Positive
True Negative faults TN: 6 FP: 1
True Positive faults FN: 1 TP: 2
Accuracy: rate of correct predictions.
Accuracy = (TN + TP) / # Assets
Accuracy = (6 + 2) / 10 = 80%

17. Applying Machine Learning to
Vibration Analysis

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1.Anomaly
2.Diagnostics
3.Prognostics
Slide 18
Applying Machine Learning to Vibration Analysis
Continuous Learning Feedback – Repair Actions, RCA, P-F Interval, MTBF, MTTR, etc.
SKF Machine Learning
Repair Action
Process Optimisation
Features: Vibration, Process, Temperature, Inspection, etc.
Alarm Alarm
Labels: Maintenance Events, Diagnostics, Root-Cause, etc.
Failure Failure

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Time
EU
Failure Mode: Bearing Defective Early Stage
Failure Effect: Increase Envelope Acc.
Proactive Action: Re-lubricate Bearing and
Closely Monitor
Current
Temperature
Pressure
Flow
Vibration Velocity
Vibration Envelope Acc.
Pattern Recognition
Features
Label

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Applying Machine Learning to Wind Turbines

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Applying Machine Learning to Wind Turbines
• Generator Drive End
• Overall Envelope F2 P2P
Bearing fault reported
by VA specialist

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Features
0 200 400 600 800 1000 1200
900
1000
1100
1200
1300
1400
1500
1600
1700
Shaft Speed
0 200 400 600 800 1000 1200
0
10
20
30
40
50
60
70
80
Overall P2P
0 1 2 3 4 5 6 7 8
104
0
500
1000
1500
2000
Generator Output Power
0 1 2 3 4 5 6
104
0
5
10
15
20
25
Wind Speed

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Generator, Outer Race Bearing Fault - Prediction Error

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SKF Machine Learning Solution

25. Digitalization and Machine
Learning Challenges

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What data is even relevant?
Ø ‘Domain knowledge’ = knowing your process and machinery,
and what features/data makes sense
Do you need real-time signals from sensors and
control systems?
Ø Domain knowledge, again
Is the quality of the relevant data OK?
Ø Labeled, comparable and digitally available
Are necessary IT systems compatible and allowed to
interact?
Ø IT security consequence analysis
It requires new ways of working not only technology.
Ø Leadership, cultural transformation, agile, collaboration
& partnership, others.
Challenges

27. Conclusions

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Growing knowledge
• Over 2 million bearings connected to the Cloud.
• Data leading enhanced analytics to understand product
performance and system design requirements better.
• Greater accuracy and better predictions.
Improving Performance
• Combination of condition monitoring data with the process,
maintenance, supply chain and other data will further improve
performance.
• Performance-driven business models and interconnected value
chain.
Self Replacement
• Smart sensors automatically diagnose and sends a message
through the supply chain.
• Self-diagnosis to self-ordering could bring stock levels close to 0.
Conclusions