Automated Machine Part Damage Detection via Displacement Imaging
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Solution Overview
Problem
Current methods for detecting damaged or missing machine parts, such as those on mining shovels, are inefficient and do not provide real-time monitoring, leading to potential equipment damage and increased downtime.
Innovation Solution
A system utilizing image analysis and computer vision techniques, including a camera to capture images of the machine part and a processor to generate displacement images, compares these images with a model to identify damaged or missing parts, providing immediate alerts through visual or audible outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image analysis and computer vision techniques are used to detect damaged or missing machine parts in real-time, then equipment damage is prevented and operational efficiency is improved, but system complexity and cost increase
Solution Approach 1:
The patent replaces manual inspection methods with automated image analysis and computer vision techniques. A camera captures images of machine parts (such as bucket teeth on mining equipment), and a processor automatically analyzes these images to detect damaged or missing parts, eliminating the need for manual visual inspection and providing real-time monitoring capability.
Solution Approach 2:
The system creates a digital copy (image) of the machine part and compares it against a reference model or previous images. The processor generates an image difference that highlights changes or anomalies, allowing detection of damage without physically contacting or altering the actual part.
2Productivity
If continuous real-time monitoring of machine parts is implemented, then downtime is minimized and productivity is improved, but energy consumption and operational costs increase
Solution Approach 1:
The system performs image capture and analysis at periodic intervals or at key moments during machine operation (such as when the bucket is in a specific position). This periodic monitoring provides real-time detection capability while avoiding continuous energy consumption, balancing productivity improvement with energy efficiency.
3Device complexity
If manual inspection methods are used to detect damaged machine parts, then system complexity is minimized, but detection time increases and productivity decreases
Solution Approach 1:
The system enables the machine to self-monitor its own condition through automated image capture and analysis. The processor automatically compares captured images against reference data and generates alerts for damaged or missing parts, eliminating the need for separate manual inspection processes and reducing detection time without requiring complex additional hardware.
Data Source
AI summary
A system and a method for detecting a damaged or missing machine part. The system includes an image capturing device for capturing images of the machine and a processor for processing the captured images. The system may further include a sensible output for providing an indication of a damaged or missing machine part. The method includes capturing images of the machine against a background which moves relative to the machine over time, selecting a pair of time-separated images from the captured images, generating a displacement image from the pair of images, comparing the machine from the displacement image with a machine model, and identifying a damaged or missing machine part from the comparison of the displacement image with the machine model. The method may further include providing a sensible output which indicates a damaged or missing machine part.