Rope Damage Evaluation Using Visual AI for Residual Strength
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Solution Overview
Problem
Current manual visual inspection methods for industrial ropes are labor-intensive, subjective, and unreliable, leading to unpredictable identification of damage and potential safety risks due to undetected failures.
Innovation Solution
A system utilizing a multi-layered neural network trained on high-resolution visual data to analyze rope damage and estimate residual break strength (RBS) by clustering damage types and correlating with historical data for real-time prediction and maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual visual inspection is used to identify rope damage, then labor cost is reduced and simplicity is maintained, but inspection reliability and consistency deteriorate due to subjective interpretation
Solution Approach 1:
The patent replaces the manual visual inspection system with an automated optical inspection system that uses cameras and image processing algorithms to detect and assess rope damage. This substitution eliminates human subjectivity while maintaining inspection simplicity, as the automated system consistently applies predefined damage criteria without fatigue or variation in judgment.
Solution Approach 2:
The system creates digital copies (images) of the rope surface to analyze damage characteristics. By capturing high-resolution images and processing them through algorithms, the system can repeatedly examine the same damage features without physical contact, ensuring consistent measurement and assessment across multiple inspections while preserving the original rope integrity.
2Device complexity
If manual inspection is performed to assess rope damage, then equipment complexity is minimized, but measurement precision and damage quantification accuracy deteriorate
Solution Approach 1:
The patent transitions from two-dimensional visual assessment to three-dimensional damage characterization by capturing images from multiple angles and depths. The system analyzes damage volume, strand displacement, and cross-sectional area affected, providing precise quantitative measurements that go beyond simple visual observation while managing complexity through systematic multi-view acquisition.
Solution Approach 2:
The system performs preliminary damage detection and classification before detailed quantification. By first identifying potential damage zones through initial image processing, the system can then focus computational resources on precise measurement of those specific areas, improving overall measurement precision without requiring complex processing of the entire rope surface uniformly.
3Loss of energy
If periodic manual inspection is conducted to ensure safety, then resource consumption is reduced, but detection speed and response time deteriorate
Solution Approach 1:
The patent enables continuous or near-continuous inspection by automating the detection process. The optical system can rapidly scan through large volumes of rope material without interruption, maintaining constant surveillance capability. This continuous operation dramatically increases detection speed compared to periodic manual inspections while the automated processing keeps resource consumption manageable through efficient algorithm execution.
Solution Approach 2:
The system extracts only the critical damage features from the full image data set using targeted image processing algorithms. By focusing computational effort on identifying and measuring specific damage characteristics rather than analyzing every pixel uniformly, the system achieves rapid detection speeds while maintaining low resource consumption through selective processing of relevant information.
Data Source
AI summary
In an embodiment of the disclosed principles, a method of analyzing a rope to estimate its residual break strength (RBS) is provided. The method entails training a multi-layered neural network to recognize rope damage and estimate an RBS for a rope under study by collecting high-resolution visual data of a plurality of sample ropes, extracting visual features of damaged areas of each rope, resolving each visual feature into a damage type and clustering damage type exemplars, breaking each sample rope to determine an actual RBS for each rope and classifying the visual data via determined RBS, specific to product type/damage mode. Job and product data are entered for the rope under study, the rope is paid out, and high-resolution visual data is captured while spooling back the rope. The correlated data is provided to the multi-layered neural network to generate an estimate of RBS for the rope under study.