This complete presentation will reveal my work on predictive maintenance on component level simulation and prediction through simulation of Digital twin. Due to the paradigm shift in the manufacturing and aviation maintenance fields to preempt the future and growth potential in digital maintenance. Doubts pls connect me through linkedin:www.linkedin.com/in/barathkumar861998 mailID:barathkumar412@outlook.com

1. Predictive
Maintenance of
Bearing through
Digital twins
By
Barath kumar S
B.Tech Mechanical Engineer

2. Project Workflow
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3. Modelling and Simulation
Thermo-Mechanical Model of the Bearing proposed
Frictional Moment Equation: Heat Flow equation:
Where,
M – Frictional moment
N – Speed of the bearing in RPM
P- Load on the bearing (N)
D- Mean diameter of the bearing
f- Friction factor of the bearing
v- Dynamic viscosity of the lubricant
(m2/s)
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4. Modelling and Simulation
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5. Reduced Order Model
Bearing 6203
Speed In Rpm
Bearing 6203
Max.temperature
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 A Reduced Order Model (ROM) is a
simplification of a high-fidelity
dynamical model that preserves
essential behavior and dominant
effects.
 For the purpose of reducing solution
time or storage capacity required for
the more complex model.

6. IoT Phase
Contactless
Temperature Sensor
(MLX 90614)
Arduino Wifi Module
(ESP 8266)
Thing-speak server
integration
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7. Circuit Diagram
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8. Hardware
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Connected Circuit
ESP8266 Wifi Module
Mlx90614 I2C sensor

9. Matlab Optimization
Algorithm
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10. Digital Twin
Meta data
Context and Characteristics
Size
Gauge
Material
Geometry
Temperature
Environment
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11. Digital twin data
Meta data of
Bearing SKF 6203
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12. Digital twin data
Raw temperature
data of Bearing
SKF 6203
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13. Twin Building Architecture
Thingspeak
data
• Speed data
• Load condition
ROM
Life
degradation
algorithm
• Smoothened
algorithm for
better result
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14. Twin Building sample
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15. Life degradation Model
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16. Life degradation results
Remaining Life of Bearing:
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17. Remaining Useful Lifetime
 Remaining useful lifetime of the bearing for
different speed and different load conditions are
taken into account.
 Prediction of failure state can be optimized
 Increasing smart decision making of physical
asset
 RUL can be easily integrate with the
maintenance schedule of the industry
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18. Thank you!
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