Sucker rod straightness measurement method based on probability statistics of edge point detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study enhances sucker rod straightness measurement using probability and statistics to filter noise, achieving higher accuracy than traditional methods for industrial applications.
Area Of Science
- Mechanical Engineering
- Metrology
- Computer Vision
Background
- Traditional straightness measurement methods lack efficiency and real-time capabilities for industrial needs.
- Machine vision offers online, non-destructive testing but struggles with environmental noise affecting accuracy.
Purpose Of The Study
- To optimize straightness measurement accuracy in industrial settings.
- To develop a noise-robust method for detecting sucker rod straightness.
Main Methods
- Utilizing probability t-distribution for data analysis.
- Implementing statistical methods to select edge points and remove scene noise.
- Employing a machine vision system with 10 industrial camera arrays.
Main Results
- The probability and statistics method strictly extracts contour sampling points with 95% confidence.
- Achieved higher accuracy in straightness detection compared to the traditional Hough transform.
- Demonstrated improved performance in noisy industrial environments.
Conclusions
- The proposed probability and statistics-based method significantly enhances the accuracy of online straightness detection.
- This approach offers a more reliable solution for high-precision machining parameter measurement.
- The method effectively mitigates noise interference in industrial machine vision applications.
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