| Description |
This Online Training (OLT) course provides a fundamental understanding of how a Digital Twin can be used as part of a Condition-Based Maintenance Plus (CBM+) capability to predictively manage the maintenance of a system. The course covers the concept of a Digital Twin and its role in predictive maintenance management. It also explores how sensor data and maintenance data can be analyzed and interpreted to predict future failures in a system. By the end of the course, learners will understand the key principles of effectively implementing predictive maintenance strategies using Digital Twins.
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Objectives
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Download Course Objectives
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Target Attendees
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Life cycle logisticians and other members of the acquisition and sustainment workforce involved in implementing Digital Twins and Condition-Based Maintenance Plus (CBM+).
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Prerequisite(s)
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None
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Predecessor Course(s)
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| Predecessor |
Predecessor Course Title |
PDS Code |
Expires On |
| None |
None |
None |
None |
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Course Length
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Approximately 2 hours to complete
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Additional Course Information
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| Delivery Mode |
Online Training
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| Equivalent Courses |
None |
| Availability |
All
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| PDS Code |
AYL |
| Walk-ins Authorized |
N/A
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| Pre-work required |
No
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| First Offering |
8/2/2024
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| Required for: |
None
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| ACE Recommended Credits |
None |
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Continuous Education Units |
0 |
| Continuous Learning Points |
2 |
| Reservist Retirement Points |
1 |
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Historical Allocations |
Mouse Over for Past CEU/CLPs
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| Fulfillment Eligible |
N/A
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| Technical Requirements |
Click Here |
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Notes
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Additional Information:
Additional Information:
- Any exams in this course must be passed with a minimum score of 80%.
- There is no time limit for completing this course.
- After completing the course, please be sure to complete the survey at the end.
- The CLPs assigned to this learning activity may be used to meet professional certification and/or licensure requirements (PDUs/CPE credits).
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