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Pattern Recognition in Wear Debris: Leveraging Computer Vision for Tribological Forensics Quiz

Posted on April 20, 2026 by

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Question 1 of 7
1. Question
Which key advantage does automated wear debris recognition provide over manual microscopy for industrial reliability?
It improves diagnostic speed and consistency at scale
It guarantees zero false alarms across all assets
It removes the need for human experts entirely
It eliminates the need for proper sample preparation
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Question 2 of 7
2. Question
Which particle morphology is most indicative of cutting or abrasive wear?
Rounded dull particles with pitting
Elongated curls and sharp slivers
Thin platelets with smear marks
Laminar fragments with step like facets
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Question 3 of 7
3. Question
What is the primary reason to separate dataset splits by asset and time period?
To reduce the diversity of imaging conditions
To increase the number of training samples
To simplify model hyperparameter tuning
To avoid optimistic bias due to data leakage
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Question 4 of 7
4. Question
Which combination best describes essential evaluation for a multi class wear debris classifier?
Precision and recall per class with confusion analysis
Bleed through and ghosting metrics
Mean squared error and R squared
Overall accuracy only
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Question 5 of 7
5. Question
Why is probability calibration important when deploying wear debris models in maintenance workflows?
It automatically balances the dataset
It aligns decision thresholds with risk tolerance
It reduces the need for illumination control
It ensures model outputs sum to one across classes
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Question 6 of 7
6. Question
Which practice strengthens trust and adoption of automated debris classification?
Removing metadata to simplify the dataset
Training only on one type of lubricant for consistency
Using saliency maps and routing uncertain cases to experts
Hiding low confidence predictions from analysts
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Question 7 of 7
7. Question
In the turbine case study, which signal justified early maintenance intervention?
A calibrated rise in fatigue class counts over time
A decrease in corrosive particles after an oil change
An immediate spike in adhesive class with no trend
A static total particle count above a fixed limit
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